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Professor Sean He

Biography

Professor Xiangjian He, as a Chief Investigator has received various research grants including four national Research Grants awarded by Australian Research Council (ARC).

He is the Director of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) and a co-leader of the Network Security research team at the Centre for Real-time Information Networks (CRIN), at the University of Technology, Sydney (UTS). He is also the Director of UTS-NPU International Joint Laboratory on Digital Media and Intelligent Networks. 

He is an IEEE Senior Member and an IEEE Signal Processing Society Student Committee member. He has been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He has been carrying out research mainly in the areas of image processing, network security, pattern recognition, computer vision and machine learning in the previous years. He is a leading researcher for image processing based on hexagonal structure. He has played various chair roles in many international conferences such as ACM MM, MMM,  IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE TrustCom, IEEE ICPR and IEEE ICARCV.

In recent years, he has many high quality publications in IEEE Transactions journals such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Cloud Computing, IEEE Transactions on Reliability, IEEE Transactions on Consumer Electronics, and in Elsevier’s journals such as Pattern Recognition, Signal Processing, Neurocomputing, Future Generation Computer Systems, Computer Networks, Computer and System Sciences, Network and Computer Applications. He has also had papers published in premier international conferences and workshops such as ACL, IJCAI, CVPR, ECCV, ACM MM, TrustCom and WACV. 

He has recently been a guest editor for various international journals such as Journal of Computer Networks and Computer Applications (Elsevier) and Signal Processing (Elsevier). He is currently an Advisor of HKIE Transactions.

He has been a supervisor of postdoctoral research fellows and PhD students.

Since 1985, he has been an academic, a visiting professor, an adjunct professor, a postdoctoral researcher or a senior researcher in various universities/institutions including Xiamen University, China, Shanghai Jiaotong University, China, University of New England, Australia, University of Georgia, USA, Electronic and Telecommunication Research Institute (ETRI) of Korea, University of Aizu, Japan, Hongkong Polytechnic University, and Macau University.

Professional

Senior Member of IEEE

ACM Member

Image of Sean He
Professor of Computer Science, School of Computing and Communications
Core Member, CRIN - Centre for Realtime Information Networks
Core Member, Global Big Data Technologies
Core Member, AAI - Advanced Analytics Institute
BSc (XMU), GradCertHEd (UTS), MSc (FZU), MSc (Flinders ), PhD (UTS)
 
Phone
+61 2 9514 1816

Research Interests

Prof He’s research is mainly in the areas of ‘Computer Vision and Pattern Recognition’ (including his recent research on network intrusion detection using computer vision and pattern recognition techniques), and ‘Hexagonal Structure for Image Processing’. The two areas create great synergies as the accurate and real-time requirements of object recognition motivates the development of image structure that better reflects the human retina, and the progress in the image structure sets new directions for image or video based research. His significant contributions on these two areas are demonstrated as follows.

1. For the research on computer vision and pattern recognition, his research team leads the research in the world on Automatic (Car) License Plate Recognition (ALPR) under complex conditions with both camera and scene changing. The ALPR software developed from my ARC-Discovery project has been applied to an NSW RTA (a previous state government agency) and DEST (previous Department of Education, Science and Technology of Australia) co-funded project for bridge maintenance in NSW. Several of his papers have been cited in a review paper on license plate recognition in IEEE Transactions on Intelligent Transportation Systems (Vol. 9, No. 3, Sep. 2008, pp.377-391), a leading journal in the area and his related work has been cited hundreds of times. His work on ALPR has been featured not only in the ARC’s Discovery Bulletin (2009 Summer) but also in the NSW Office of Science & Medical Research newsletter and at UTS Homepage in 2009. The report of his work was also noticed to Australian Parliament in October 2009. The importance of his research results has been demonstrated by their potential applications to defense, anti-terrorism and others.

2. In the previous years, he has established a Network Security Research team at UTS and successfully integrated the techniques used on computer vision and pattern recognition, such as Support Vector Machine (SVM), Earth Mover’s Distance (EMD), Mahalanobis Distance Map (MDM) and multivariate analysis into the research in the areas of network security. He and his team have applied these techniques, for the first time, to the work for network intrusion detection. He has successfully supervised two PhD students to completion working on network intrusion detection, and has very recently published several high quality papers in top (all ERA Tier A*/A ranked) journals such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, Computer Networks (Elsevier), Signal Processing (Elsevier) and Future Generation Computer Systems (Elsevier). In 2014, he published a pioneering research paper in IEEE Cloud Computing Magazine proposing the framework of a collaborative network security system, for the first time, for enhancing big data security in cloud environment.

3. About his research on hexagonal structure for image processing, he is a pioneer and a world leading researcher working on the novel Spiral Architecture (a hexagonal image structure) to increase image processing speed and accuracy. Their most current results for conversion between the traditional square pixel structure and the novel hexagonal structure was disclosed to UniQuest (a UTS commercial arm) for invention registration. As reported in the ARC’s Discovery Bulletin (2009 Summer), 'The beauty of hexagonal pixels is that they can provide equivalent picture quality using 13 per cent fewer pixels', and ‘the potential is enormous—it could for instance provide improved resolution for still and moving digital cameras and could find many applications improving the object recognition capabilities of robots.’ His results for this research have also been cited hundreds of times.

To recognise his outstanding contributions in his research field including the two research areas mentioned above, he has been awarded an internationally recognized certificate, 'Internationally Registered Technology Specialist' (Level 7, registration no.: IR7-02062418) by the International Technology Institute (ITI). ITI is an International Technical Society, and acts as a secretariat for recognition of engineers, scientists and technologists. This certificate recognises his expertise in the research areas of Computer Vision, Image Processing, and Distributed and Parallel Computing. This level of certificate was awarded for 'outstanding recognition by qualified establishments, publications, awards, patents, positions, success record' as described by ITI.

Can supervise: Yes
Principal Supervisor of PhD students and supervisor of Postdoctoral Research Fellows.

Internetworking subjects for undergraduate and postgraduate students.

Chapters

He, X., Luo, S., Tao, D., Xu, C., Yang, J. & Abul Hasan, M. 2015, 'Preface' in MultiMedia Modeling, Springer, Germany, pp. V-VI.
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He, X., Xu, C., Tao, D., Luo, S., Yang, J. & Hasan, M.A. 2015, 'Preface' in MultiMedia Modeling (LNCS), Springer, Germany, pp. V-VI.
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Zeng, C., Jia, W., He, S. & Xu, M. 2013, 'Recent Advances on Graph-Based Image Segmentation Techniques' in Bai, X., Cheng, J. & Hancock, E. (eds), Graph-Based Methods in Computer Vision: Developments and Applications, IGI Global, Hershey, Pennsylvania (USA), pp. 140-154.
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Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter.
Zeng, C., He, X., Jia, W. & Xu, M. 2013, 'Recent advances on graph-based image segmentation techniques' in Image Processing: Concepts, Methodologies, Tools, and Applications, pp. 1323-1337.
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© 2013, IGI Global. Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter.
Zeng, C., Jia, W., He, X. & Xu, M. 2012, 'Recent advances on graph-based image segmentation techniques' in Graph-Based Methods in Computer Vision: Developments and Applications, pp. 140-154.
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Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter. © 2013, IGI Global.
Chen, Y., Wu, Q. & He, S. 2011, 'Human Action Recognition Based on Radon Transform' in Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E. & Wang, H. (eds), Studies in Computational Intelligence vol 346. Multimedia Analysis, Processing and Communications, Springer-Verlag Berlin / Heidelberg, Berlin/Heidelberg, pp. 369-389.
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A new feature description is used for human action representation and recognition. Features are extracted from the Radon transforms of silhouette images. Using the features, key postures are selected. Key postures are combined to construct an action template for each action sequence. Linear Discriminant Analysis (LDA) is applied to obtain low dimensional feature vectors. Different classification methods are used for human action recognition. Experiments are carried out based on a publicly available human action database.
Tran, T.P., Tsai, P.C., Jan, T. & He, S. 2010, 'Machine Learning Techniques for Network Intrusion Detection' in Shawkat Ali, A.B.M. & Xiang, Y. (eds), Dynamic and Advanced Data Mining for Progressing Technological Development, IGI Global, New York, USA, pp. 273-299.
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Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing prevention techniques such as encryption and firewalls, Intrusion Detection System (IDS) has established itself as an emerging technology that is able to detect unauthorized access and abuse of computer systems by both internal users and external offenders. Most of the novel approaches in this field have adopted Artificial Intelligence (AI) technologies such as Artificial Neural Networks (ANN) to improve performance as well as robustness of IDS. The true power and advantages of ANN lie in its ability to represent both linear and non-linear relationships and learn these relationships directly from the data being modeled. However, ANN is computationally expensive due to its demanding processing power and this leads to overfitting problem, i.e. the network is unable to extrapolate accurately once the input is outside of the training data range. These limitations challenge IDS with low detection rate, high false alarm rate and excessive computation cost. This chapter proposes a novel Machine Learning (ML) algorithm to alleviate those difficulties of existing AI techniques in the area of computer network security. The Intrusion Detection dataset provided by Knowledge Discovery and Data Mining (KDD-99) is used as a benchmark to compare our model with other existing techniques. Extensive empirical analysis suggests that the proposed method outperforms other state-of-the-art learning algorithms in terms of learning bias, generalization variance and computational cost. It is also reported to significantly improve the overall detection capability for difficult-to-detect novel attacks which are unseen or irregularly occur in the training phase.
Nanda, P. & He, S. 2010, 'Scalable Internet Architecture Supporting Quality of Service (QoS)' in etal, K.C.L. (ed), The Handbook of Research on Scalable Computing Technologies, IGI Global, USA, pp. 339-357.
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The evolution of Internet and its successful technologies has brought a tremendous growth in business, education, research etc. over the last four decades. With the dramatic advances in multimedia technologies and the increasing popularity of real-time applications, recently Quality of Service (QoS) support in the Internet has been in great demand. Deployment of such applications over the Internet in recent years, and the trend to manage them efficiently with a desired QoS in mind, researchers have been trying for a major shift from its Best Effort (BE) model to a service oriented model. Such efforts have resulted in Integrated Services (Intserv), Differentiated Services (Diffserv), Multi Protocol Label Switching (MPLS), Policy Based Networking (PBN) and many more technologies. But the reality is that such models have been implemented only in certain areas in the Internet not everywhere and many of them also faces scalability problem while dealing with huge number of traffic flows with varied priority levels in the Internet. As a result, an architecture addressing scalability problem and satisfying end-to-end QoS still remains a big issue in the Internet. In this chapter the authors propose a policy based architecture which they believe can achieve scalability while offering end to end QoS in the Internet.
Wu, Q. & He, S. 2010, 'Image Partitioning on Spiral Architecture' in etal, K.C.L. (ed), The Handbook of Research on Scalable Computing Technologies, IGI Global, USA, pp. 808-840.
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Spiral Architecture is a relatively new and powerful approach to image processing. It contains very useful geometric and algebraic properties. Based on the abundant research achievements in the past decades, it is shown that Spiral Architecture will play an increasingly important role in image processing and computer vision. This chapter presents a significant application of Spiral Architecture for distributed image processing. It demonstrates the impressive characteristics of spiral architecture for high performance image processing. The proposed method tackles several challenging practical problems during the implementation. The proposed method reduces the data communication between the processing nodes and is configurable. Moreover, the proposed partitioning scheme has a consistent approach: after image partitioning each sub-image should be a representative of the original one without changing the basic object, which is important to the related image processing operations.
Xu, M., He, S., Jin, J., Peng, Y., Xu, C. & Guo, W. 2010, 'Using Scripts for Affective Content Retrieval' in Qiu, G., Lam, K.M., Kiya, H., Xue, X.Y., Kuo, C.C.J. & Lew, M.S. (eds), Lecture Notes in Computer Science 6298 - Advances in Multimedia Information Processing - PCM 2010, Springer, Germany, pp. 43-51.
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Movie affective content analysis attracts increasing research efforts since affective content not only affect users attentions but also locate movie highlights. However, affective content retrieval is still a challenging task due to the limitation of affective features in movies. Scripts provide direct access to the movie content and represent affective aspects of the movie. In this paper, we utilize scripts as an important clue to retrieve video affective content. The proposed approach includes two main steps. Firstly, affective script partitions are extracted by detecting emotional words. Secondly, affective partitions are validated by using visual and auditory features. The results are encouraging and compared with the manually labelled ground truth.
Ye, Y., He, S., Li, J., Jia, W. & Wu, Q. 2009, 'Image Transformation on Hexagonal Structure Based on Conversion between 1D and 2D Coordinates' in Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S. & Wang, G. (eds), Rough Sets and Knowledge Technology, Springer, Berlin, Germany, pp. 571-578.
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Spiral Architecture, a hexagonal image structure is a novel and powerful approach to machine vision system. The pixels on Spiral architecture are geometrically arranged using a 1D (Spiral) addressing scheme in an ascending order along a spiral-like curve. Spiral addition and Spiral multiplication are defined based on the Spiral addresses on Spiral Architecture. These two fundamental operations result in fast and easy translation, rotation and separation on images, and hence play very important roles for image processing on Spiral Architecture. Moreover, 2D coordinates according to rows and columns defined on Spiral Structure provide a good mapping to the ordinary 2D coordinates defined on the common square image structure. Therefore, how to convert the 1D Spiral addresses from and to the 2D coordinates on Spiral Architecture has become very important to apply the theory developed on a hexagonal image structure for image processing (e.g., rotation). In this paper, we perform a fast way to correctly locate any hexagonal pixel when its Spiral address is known, and compute the Spiral address of any hexagonal pixel when its location is known. As an illustration of the use of conversions, we demonstrate the accurate image translation and rotation using experimental results.
Zheng, L., He, S. & Hintz, T.B. 2007, 'Comparison of SVMs in Number Plate Recognition' in Singh, S. & Singh, M. (eds), Progress in Pattern Recognition, Springer, London, pp. 152-160.
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Loo, J. 2007, 'Preface', pp. 201-202.

Conferences

Zhou, Z., Li, K., He, X.S. & L, M. 2016, 'A Generative Model for Recognizing Mixed Group Activities in Still Images', Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 25th International Joint Conference on Artificial Intelligence (IJCAI-16), AAAI Press, New York, pp. 3654-3660.
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Gu, K., Liu, M., Zhou, T., Liu, F., He, X.S., Yang, J. & Qiao, Y. 2016, 'Patch-based Object Tracking via Locality-constrained Linear Coding', 35th Chinese Control Conference (CCC2016), Chengdu, China.
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Aldebei, K., He, X.S., Jia, W. & Yang, J. 2016, 'Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model', 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.
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Usman, M., Jan, M., He, X.S. & Nanda 2016, 'Data Sharing in Secure Multimedia Wireless Sensor Networks', 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16), Tianjin, China.
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Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2016, 'An Improvement of Tree-Rule Firewall for a Large Network: Supporting Large Rule Size and Low Delay', 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16), Tianjin, China.
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Li, W., Yao, J., Dong, T., Li, H. & He, X. 2016, 'Moving vehicle detection based on an improved interframe difference and a Gaussian model', Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015, pp. 969-973.
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© 2015 IEEE.For moving vehicle detection, this paper presents an algorithm on the basis of an improved interframe differential algorithm and an improved Gaussian model. Firstly, according to a statistical histogram, an interesting region is extracted. Through a mean algorithm, an initial background model is established. The interesting region is divided into several blocks by a self-adaptive method. Secondly, according to an improved interframe difference algorithm, the interesting region is separated roughly. On the basis of these steps, we utilize an improved Gaussian model to separate the rough results precisely. At last, the results are processed by double-threshold background subtracting. Experimental results show this algorithm can detect moving vehicles rapidly and accurately.
Zhang, F., Li, J., Li, F., Xu, M., Xu, Y. & He, X. 2015, 'Community detection based on links and node features in social networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 21st International Conference on Multimedia Modelling, MMM 2015, Springer, Sydney, Australia, pp. 418-429.
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© Springer International Publishing Switzerland 2015. Community detection is a significant but challenging task in the field of social network analysis. Many effective methods have been proposed to solve this problem. However, most of them are mainly based on the topological structure or node attributes. In this paper, based on SPAEM [1], we propose a joint probabilistic model to detect community which combines node attributes and topological structure. In our model, we create a novel feature-based weighted network, within which each edge weight is represented by the node feature similarity between two nodes at the end of the edge. Then we fuse the original network and the created network with a parameter and employ expectation-maximization algorithm (EM) to identify a community. Experiments on a diverse set of data, collected from Facebook and Twitter, demonstrate that our algorithm has achieved promising results compared with other algorithms.
Dai, M., Lin, P., Wu, L., Chen, Z., Lai, S., Zhang, J., Cheng, S. & He, X. 2015, 'Orderless and Blurred Visual Tracking via Spatio-temporal Context', MultiMedia Modeling, Springer, pp. 25-36.
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Al-Dmour, H., Ali, N. & Al-Ani, A. 2015, 'An Efficient Hybrid Steganography Method Based on Edge Adaptive and Tree Based Parity Check', MultiMedia Modeling (LNCS), 21st International Conference on MultiMedia Modelling, MMM 2015, Springer, Sydney, Australia, pp. 1-12.
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A major requirement for any steganography method is to minimize the changes that are introduced to the cover image by the data embedding process. Since the Human Visual System (HVS) is less sensitive to changes in sharp regions compared to smooth regions, edge adaptive has been proposed to discover edge regions and enhance the quality of the stego image as well as improve the embedding capacity. However, edge adaptive does not apply any coding scheme, and hence it embedding efficiency may not be optimal. In this paper, we propose a method that enhances edge adaptive by incorporating the Tree-Based Parity Check (TBPC) algorithm, which is a well-established coding-based steganography method. This combination enables not only the identification of potential pixels for embedding, but it also enhances the embedding efficiency through an efficient coding mechanism. More specifically, the method identifies the embedding locations according to the difference value between every two adjacent pixels, that form a block, in the cover image, and the number of embedding bits in each block is determined based on the difference between its two pixels. The incorporation of TBPC minimizes the modifications of the cover image, as it changes no more than two bits out of seven pixel bits when embedding four secret bits. Experimental results show that the proposed scheme can achieve both large embedding payload and high embedding efficiency.
Ambusaidi, M.A., He, X. & Nanda, P. 2015, 'Unsupervised Feature Selection Method for IntrusionDetection System', Proceedings of the IEEE 14th International Conference on Trust, Security and Privacy in Computing and Communications, Trustcom 2015, IEEE 14th International Conference on Trust, Security and Privacy in Computing and Communications, Trustcom 2015, IEEE, Helsinki, Finland, pp. 295-301.
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This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an Extended Laplacian score, EL in short. Specifically, two main phases are involved in EL to complete the selection procedures. In the first phase, the Laplacian score algorithm is applied to select the features that have the best locality preserving power. In the second phase, EL proposes a Redundancy Penalization (RP) technique based on mutual information to eliminate the redundancy among the selected features. This technique is an enhancement over Battiti's MIFS. It does not require a user-defined parameter such as beta to complete the selection processes of the candidate feature set as it is required in MIFS. After tackling the feature selection problem, the final selected subset is then used to build an Intrusion Detection System. The effectiveness and the feasibility of the proposed detection system are evaluated using three well-known intrusion detection datasets: KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results confirm that our feature selection approach performs better than the Laplacian score method in terms of classification accuracy.
Jan, M., Nanda, P., He, X. & Liu, R.P. 2015, 'A Sybil Attack Detection Scheme for a Centralized Clustering-based Hierarchical Network', IEEE Computer Society, The 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-15), IEEE, Helsinki, Finland, pp. 318-325.
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Aldebei, K.W., He, X. & Yang, J. 2015, 'Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model', Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers), International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Beijing, China, pp. 501-505.
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This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and the accuracy of the classification process of our method. Experimental results show that the proposed method outperforms two state-of-the-art methods. © 2015 Association for Computational Linguistics.
Li, M., Xu, Y.D. & He, X.J. 2015, 'Face hallucination based on Nonparametric Bayesian learning', Proceedings of IEEE International Conference on Image Processing, IEEE International Conference on Image Processing, IEEE, Quebec City, Canada, pp. 986-990.
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In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination.
Maleki, B., Ebrahimnezhad, H., Xu, M. & He, X. 2015, 'Hand Gesture Recognition for a Virtual Mouse Application Using Geometric Feature of Finger's Trajectories', Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, International Conference on Internet Multimedia Computing and Service, ACM, USA.
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We aim to enable a computer to comprehend and perform the mouse functions by analyzing a video with hand motions. For this purpose, dynamic gestures are captured by a web cam and are recognized as pre-defined gestures which are used to suggest mouse functions. The proposed algorithm initially detects the hand. Then, it tracks fingertips' trajectories within a frame sequence. Finally, hand gestures are recognized through computing a set of proposed geometric features of fingers' trajectories and comparing with our collected gestures dataset. In this paper, four types of descriptors are defined for a dynamic gesture. Each descriptor includes different number of features, which compose a feature vector with 135 dimensions. Different classification algorithms (e.g. KNN, LDA, Nave Bayes and SVM) are applied to compare the detection results. The minimal misclassification error rate (MCR) reaches about 4% (i.e. Correct Recognition rate of 96%). Furthermore, we applied Principle Component Analysis (PCA) to reduce the number of features. With 30 dimensional features (principle components), LDA classifier can achieve about 0.09% misclassification error rate.
Xie, K., Fu, K., Zhou, T., Yang, J., Wu, Q. & He, X. 2015, 'Small target detection using an optimization-based filter', Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, South Brisbane, Australia, pp. 1583-1587.
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Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter.
Johannes, A., Nanda, P. & He, X. 2015, 'Resource Utilization Based Dynamic Pricing Approach on Cloud Computing Application', Springer International Publishing, 15th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), Springer International Publishing, Zhangjiajie, China, pp. 669-677.
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Utilizing cloud-based services, users are required to first specify their goal of using such cloud based applications and then obtain service compositions satisfying their specific needs from the cloud service providers. The process involves dynamic pricing schemes for service provisioning between themselves and their cloud service providers. As a result, it is quite challenging with existing supply and demand driven approaches to ensure true dynamic resource provisioning for users with critical applications. To address this problem, we propose a game theory approach based on fuzzy logic which is then used to ensure aspects of resource provisioning on cloud. In our approach, we perform a trade-off for resources between service provider, cloud resource provider and service user based on the user demand and avoid rejecting users to ensure reliable resource provisioning. Experimental results demonstrate that our proposed approach can improve resource utilization associated with users.
Zhao, Y., He, X., Chen, B. & Zhao, X. 2015, 'Integrating simplified inverse representation and CRC for face recognition', Multi-disciplinary Trends in Artificial Intelligence (LNCS), 9th Mahasarakham International Workshop on Artificial Intelligence, Springer, Fuzhou, China, pp. 171-183.
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© Springer International Publishing Switzerland 2015. The representation based classification method (RBCM) has attracted much attention in the last decade. RBCM exploits the linear combination of training samples to represent the test sample, which is then classified according to the minimum reconstruction residual. Recently, an interesting concept, Inverse Representation (IR), is proposed. It is the inverse process of conventional RBCMs. IR applies test samples' information to represent each training sample, and then classifies the training sample as a useful supplement for the final classification. The relative algorithm CIRLRC, integrating IR and linear regression classification (LRC) by score fusing, shows superior classification performance. However, there are two main drawbacks in CIRLRC. First, the IR in CIRLRC is not pure, because the test vector contains some training sample information. The other is the computation inefficiency because CIRLRC should solve C linear equations for classifying the test sample respectively, where C is the number of the classes. Therefore, we present a novel method integrating simplified IR (SIR) and collaborative representation classification (CRC), named SIRCRC, for face recognition. In SIRCRC, only test sample information is fully used in SIR, and CRC is more efficient than LRC in terms of speed, thus, one linear equation system is needed. Extensive experimental results on face databases show that it is very competitive with both CIRLRC and the state-of-the-art RBCM.
Xu, Z., Ye, L. & He, X. 2015, 'Single-sample face recognition based on WWSRC and expanding sample', Multi-disciplinary Trends in Artificial Intelligence (LNCS), 9th International Workshop, MIWAI 2015, Springer, Fuzhou, China, pp. 197-206.
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© Springer International Publishing Switzerland 2015. This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.
Yeh, W.C., Chung, V.Y.Y., Jiang, Y.Z. & He, X. 2015, 'Solving reliability redundancy allocation problems with orthogonal simplified swarm optimization', Proceedings of the International Joint Conference on Neural Networks, International Joint Conference on Neural Networks, IEEE, Killarney, Ireland, pp. 1-7.
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© 2015 IEEE. This study applies a penalty guided strategy and the orthogonal array test (OA) based on the Simplified Swarm Optimization algorithm (SSO) to solve the reliability redundancy allocation problems (RRAP) in the series system, the series-parallel system, the complex (bridge) system, and the overspeed protection of gas turbine system. For several decades, the RRAP has been one of the most well known techniques. The maximization of system reliability, the number of redundant components, and the reliability of corresponding components in each subsystem have to be decided simultaneously with nonlinear constraints, acting as one difficulty for the use of the RRAP. In other words, the objective function of the RRAP is the mixed-integer programming problem with the nonlinear constraints. The RRAP is of the class of NP-hard. Hence, in this paper, the SSO algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. There are four RRAP problems used to illustrate the applicability and the effectiveness of the SSO. The experimental results are compared with previously developed algorithms in literature. Moreover, the maximum-possible-improvement (MPI) is used to measure the amount of improvement of the solution found by the SSO to the previous solutions. According to the results, the system reliabilities obtained by the proposed SSO for the four RRAP problems are as well as or better than the previously best-known solutions.
Sha, F., Bae, C., Liu, G., Zhao, X., Chung, Y.Y., Yeh, W. & He, X. 2015, 'A probability-dynamic Particle Swarm Optimization for object tracking', Proceedings of the International Joint Conference on Neural Networks.
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© 2015 IEEE. Particle Swarm Optimization has been used in many research and application domain popularly since its development and improvement. Due to its fast and accurate solution searching, PSO has become one of the high potential tools to provide better outcomes to solve many practical problems. In image processing and object tracking applications, PSO also indicates to have good performance in both linear and non-linear object moving pattern, many scientists conduct development and research to implement not only basic PSO but also improved methods in enhancing the efficiency of the algorithm to achieve precise object tracking orbit. This paper is aim to propose a new improved PSO by comparing the inertia weight and constriction factor of PSO. It provides faster and more accurate object tracking process since the proposed algorithm can inherit some useful information from the previous solution to perform the dynamic particle movement when other better solution exists. The testing experiments have been done for different types of video, results showed that the proposed algorithm can have better quality of tracking performance and faster object retrieval speed. The proposed approach has been developed in C++ environment and tested against videos and objects with multiple moving patterns to demonstrate the benefits with precise object similarity.
Usman, M., He, X., Xu, M. & Lam, K.M. 2015, 'Survey of Error Concealment Techniques: Research Directions and Open Issues', IEEE Picture Coding Symposium, IEEE, Cairns, pp. 233-238.
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Zhou, T., Bhaskar, H., Xie, K., Yang, J., He, X. & Shi, P. 2015, 'Online learning of multi-feature weights for robust object tracking', Proceedings - International Conference on Image Processing, ICIP, IEEE International Conference on Image Processing (ICIP), IEEE, Quebec City, Canada, pp. 725-729.
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© 2015 IEEE. Sparse Representation based Classification (SRC) and its potential in object tracking have been explored in recent years. However, the trade-off between the discriminative ability of the overly emphasized sparse representation and the lack of insight on correlation of visual information has raised questions over the general applicability of such methods in object tracking. In addition, the need for the optimization of a series of l1-regularized least square norm, increases the computational complexity thereby limiting their usage in real-time applications. In this paper, a novel approach to robust object tracking is proposed. First, the variations in the appearance of the tracked target is modelled using PCA basis vectors, and further, a l2-regularized least square method is used to solve the proposed representation model. In order to improve the robustness of feature representation in object tracking applications, weights are associated with multiple trackers; each formulated using a different feature, and adapted via an online learning scheme. Finally, a decision fusion criterion is imposed to generate an optimized output through the weighted combination of different tracking results. Experiments on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to thirteen other state-of-the-art baselines.
He, X., Luo, S., Tao, D., Xu, C., Yang, J. & Abul Hasan, M. 2015, 'MultiMedia Modeling: 21st International Conference, MMM 2015 Sydney, NSW, Australia, January 5-7, 2015 Proceedings, Part II', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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He, C., Zhang, L., He, X. & Jia, W. 2015, 'A new image decomposition and reconstruction approach -- adaptive fourier decomposition', MultiMedia Modeling (LNCS), 21st International Conference on MultiMedia Modeling, Springer, Sydney, Australia, pp. 227-236.
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© Springer International Publishing Switzerland 2015. Fourier has been a powerful mathematical tool for representing a signal into an expression consist of sin and cos. Recently a new developed signal decomposition theory is proposed by Pro. Tao Qian named Adaptive Fourier Decomposition, which has the advantage in time frequency over Fourier decomposition and without the need for a fixed window size problem such as short-time frequency transform. Studies show that AFD can fast decompose signals into positive-frequency functions with good analytical properties. In this paper we apply AFD into image decomposition and reconstruction area first time in the literature, which shows a promising result and gives the fundamental prospect for image compression.
Long, W., Yang, J., Song, D., Chen, X. & He, X. 2015, 'A novel fast full frame video stabilization via three-layer model', MultiMedia Modeling (LNCS), 21st International Conference on MultiMedia Modeling, Springer, Sydney, Australia, pp. 246-256.
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© Springer International Publishing Switzerland 2015. Video stabilization is an important video enhancement technology which aims at removing undesired shaking from input videos. A challenging task in stabilization is to inpaint the missing pixels of undefined areas in the motion-compensated frames. This paper describes a new video stabilization method. It adopts a multi-layer model to improve the efficiency of the video stabilization. The undefined areas can be inpainted in real-time. Compared with traditional methods, our proposed algorithm only need maintain a single updated mosaic image for video completion, while previous methods require to store all neighboring frames and then registered with the current frame. The experimental results demonstrated the effectiveness of the proposed approach.
Zhou, Z., Li, K. & He, X.S. 2015, 'Recognizing Human Activity in Still Images by Integrating Group-Based Contextual Cues', Proceedings of the 23rd ACM international conference on Multimedia, ACM Multimedia Conference 2015, ACM, Brisbane, Australia, pp. 1135-1138.
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Turan, C., Lam, K.M. & He, X.S. 2015, 'Facial expression recognition with emotion-based feature fusion', Proceedings of APSIPA Annual Summit and Conference 2015, APSIPA Annual Summit and Conference 2015, IEEE, Hong Kong, pp. 1-6.
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In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors for facial expression representation, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), and Pyramid of Histogram of Oriented Gradients (PHOG), are considered. Supervised Locality Preserving Projection (SLPP) is applied to the respective features for dimensionality reduction and manifold learning. Experiments show that descriptors are also sensitive to the conditions of images, such as race, lighting, pose, etc. Thus, an adaptive descriptor selection algorithm is proposed, which determines the best two features for each expression class on a given training set. These two features are fused, so as to achieve a higher recognition rate for each expression. In our experiments, the JAFFE and BAUM-2 databases are used, and experiment results show that the descriptor selection step increases the recognition rate up to 2%.
Ambusaidi, M.A., He, X. & Nanda, P. 2015, 'Unsupervised feature selection method for intrusion detection system', Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, pp. 295-301.
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© 2015 IEEE.This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an Extended Laplacian score, EL in short. Specifically, two main phases are involved in EL to complete the selection procedures. In the first phase, the Laplacian score algorithm is applied to select the features that have the best locality preserving power. In the second phase, EL proposes a Redundancy Penalization (RP) technique based on mutual information to eliminate the redundancy among the selected features. This technique is an enhancement over Battiti's MIFS. It does not require a user-defined parameter such as beta to complete the selection processes of the candidate feature set as it is required in MIFS. After tackling the feature selection problem, the final selected subset is then used to build an Intrusion Detection System. The effectiveness and the feasibility of the proposed detection system are evaluated using three well-known intrusion detection datasets: KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results confirm that our feature selection approach performs better than the Laplacian score method in terms of classification accuracy.
Jan, M.A., Nanda, P., He, X. & Liu, R.P. 2015, 'A sybil attack detection scheme for a centralized clustering-based hierarchical network', Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, pp. 318-325.
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© 2015 IEEE.Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in remote and hostile environments for monitoring applications and data collection. Miniature sensor nodes collaborate with each other to provide information on an unprecedented temporal and spatial scale. The resource-constrained nature of sensor nodes along with human-inaccessible terrains poses various security challenges to these networks at different layers. In this paper, we propose a novel detection scheme for Sybil attack in a centralized clustering-based hierarchical network. Sybil nodes are detected prior to cluster formation to prevent their forged identities from participating in cluster head selection. Only legitimate nodes are elected as cluster heads to enhance utilization of the resources. The proposed scheme requires collaboration of any two high energy nodes to analyze received signal strengths of neighboring nodes. The simulation results show that our proposed scheme significantly improves network lifetime in comparison with existing clustering-based hierarchical routing protocols.
Ambusaidi, M.A., He, X., Tan, Z., Nanda, P., Lu, L. & Nagar, U. 2014, 'A novel feature selection approach for intrusion detection data classification', 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Trustcom-2014, IEEE Computer Society, Beijing, pp. 82-89.
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Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other stateof- the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the previously reported results.
Chomsiri, He, X., Nanda, P. & Tan, Z. 2014, 'A Stateful Mechanism for the Tree-Rule Firewall', 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Trustcom-2014, IEEE Computer Society, Beijing, pp. 122-129.
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In this paper, we propose a novel connection tracking mechanism for Tree-rule firewall which essentially organizes firewall rules in a designated Tree structure. A new firewall model based on the proposed connection tracking mechanism is then developed and extended from the basic model of Netfilter's ConnTrack module, which has been used by many early generation commercial and open source firewalls including IPTABLES, the most popular firewall. To reduce the consumption of memory space and processing time, our proposed model uses one node per connection instead of using two nodes as appeared in Netfilter model. This can reduce memory space and processing time. In addition, we introduce an extended hash table with more hashing bits in our firewall model in order to accommodate more concurrent connections. Moreover, our model also applies sophisticated techniques (such as using static information nodes, and avoiding timer objects and memory management tasks) to improve its processing speed. Finally, we implement this model on Linux Cent OS 6.3 and evaluate its speed. The experimental results show that our model performs more efficiently in comparison with the Netfilter/IPTABLES.
Jan, A., Nanda, P., He, X., Tan, Z. & Liu, R. 2014, 'A Robust Authentication Scheme for Observing Resources in the Internet of Things Environment', 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Trustcom-2014, IEEE Computer Society, Beijing, pp. 205-211.
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The Internet of Things is a vision that broadens the scope of the internet by incorporating physical objects to identify themselves to the participating entities. This innovative concept enables a physical device to represent itself in the digital world. There are a lot of speculations and future forecasts about the Internet of Things devices. However, most of them are vendor specific and lack a unified standard, which renders their seamless integration and interoperable operations. Another major concern is the lack of security features in these devices and their corresponding products. Most of them are resource-starved and unable to support computationally complex and resource consuming secure algorithms. In this paper, we have proposed a lightweight mutual authentication scheme which validates the identities of the participating devices before engaging them in communication for the resource observation. Our scheme incurs less connection overhead and provides a robust defence solution to combat various types of attacks.
Wong, M.T., He, X., Yeh, W.C., Ibrahim, Z. & Chung, Y.Y. 2014, 'Feature Selection and Mass Classification Using Particle Swarm Optimization and Support Vector Machine', Neural Information Processing: 21st International Conference, ICONIP 2014, Proceedings, Part III, Lecture Notes in Computer Science, Vol. 8836, 2014, 21st International Conference on Neural Information Processing, Springer International Publishing, Kuching, Malaysia, pp. 439-446.
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This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.
Chang, P.C. & He, X. 2014, 'Macroscopic Indeterminacy Swarm Optimization (MISO) algorithm for real-parameter search', Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, pp. 1571-1578.
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© 2014 IEEE. Swarm Intelligence (SI) is a nature-inspired emergent artificial intelligence. They are often inspired by the phenomena in nature. Many proposed algorithms are focused on designing new update mechanisms with formulae and equations to emerge new solutions. Despite the techniques used in an algorithm being the key factor of the whole system, the evaluation of candidate solutions also plays an important role. In this paper, the proposed algorithm Macroscopic Indeterminacy Swarm Optimization (MISO) presents a new search scheme with indeterminate moment of evaluation. Here, we perform an experiment based on public benchmark functions. The results produced by MISO, Differential Evolution (DE) with various settings, Artificial Bee Colony (ABC), Simplified Swarm Optimization (SSO), and Particle Swarm Optimization (PSO) have been compared. The result shows MISO can achieve similar or even better performance than other algorithms.
Liu, H., Xu, M., He, X. & Wang, J. 2014, 'Estimate Gaze Density by Incorporating Emotion', ACM MM2014, 22nd ACM International Conference on Multimedia, ACM, Orlando, pp. 1113-1116.
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Gaze density estimation has attracted many research efforts in the past years. The factors considered in the existing methods include low level feature saliency, spatial position, and objects. Emotion, as an important factor driving attention, has not been taken into account. In this paper, we are the first to estimate gaze density through incorporating emotion. To estimate the emotion intensity of each position in an image, we consider three aspects, generic emotional content, facial expression intensity, and emotional objects. Generic emotional content is estimated by using Multiple instance learning, which is employed to train an emotion detector from weakly labeled images. Facial expression intensity is estimated by using a ranking method. Emotional objects are detected, by taking blood/injury and worm/snake as examples. Finally, emotion intensity, low level feature saliency, and spatial position, are fused, through a linear support vector machine, to estimate gaze density. The performance is tested on public eye tracking dataset. Experimental results indicate that incorporating emotion does improve the performance of gaze density estimation.
Zhou, T., Zhang, J., Xie, K., Yang, J. & He, X.S. 2014, 'Visual tracking based on weighted subspace reconstruction error', ICIP 2014, International Conference on Image Processing (ICIP), IEEE, Paris, pp. 461-465.
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It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven stateof-the-art methods in terms of accuracy and robustness.
Fu, K., Gong, C., Gu, I.Y.H., Yang, J. & He, X. 2014, 'Spectral salient object detection', ICME2014, IEEE International Conference on Multimedia and Expo, IEEE, Chengdu.
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Many existing methods for salient object detection are performed by over-segmenting images into non-overlapping regions, which facilitate local/global color statistics for saliency computation. In this paper, we propose a new approach: spectral salient object detection, which is benefited from selected attributes of normalized cut, enabling better retaining of holistic salient objects as comparing to conventionally employed pre-segmentation techniques. The proposed saliency detection method recursively bi-partitions regions that render the lowest cut cost in each iteration, resulting in binary spanning tree structure. Each segmented region is then evaluated under criterion that fit Gestalt laws and statistical prior. Final result is obtained by integrating multiple intermediate saliency maps. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method against 13 state-of-the-art approaches to salient object detection.
Zhou, T., He, X., Xie, K., Fu, K., Zhang, J. & Yang, J. 2014, 'Visual tracking via graph-based efficient manifold ranking with low-dimensional compressive features', ICME 2014, IEEE, Chengdu.
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In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the goal of tracking is to search the unlabeled sample that is the most relevant with existing labeled nodes by manifold ranking algorithm. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show the proposed algorithm outperforms six state-of-the-art methods in terms of accuracy and robustness.
Guo, D., Zhang, J., Xu, M., He, X., Li, M. & Zhao, C. 2014, 'A Multiple Features Distance Preserving (MFDP) Model for Saliency Detection', Bouzerdoum, International Conference on Digital Image Computing: Techniques and Applications, IEEE, Wollongong.
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Playing a vital role, saliency has been widely applied for various image analysis tasks, such as content-aware image retargeting, image retrieval and object detection. It is generally accepted that saliency detection can benefit from the integration of multiple visual features. However, most of the existing literatures fuse multiple features at saliency map level without considering cross-feature information, i.e. generate a saliency map based on several maps computed from an individual feature. In this paper, we propose a Multiple Feature Distance Preserving (MFDP) model to seamlessly integrate multiple visual features through an alternative optimization process. Our method outperforms the state-of-the-arts methods on saliency detection. Saliency detected by our method is further cooperated with seam carving algorithm and significantly improves the performance on image retargeting.
Luo, X., Wan, Y., He, X., Yang, J. & Mori, K. 2014, 'Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking', CVPR2015, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Columbus, pp. 1250-1257.
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The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from. The particle diversity plays an important role as it affects the performance of filtering. Although the condensation algorithm is widely used in computer vision, it easily gets trapped in local minima due to the particle degeneracy. We introduce a modified evolutionary computing method, adaptive differential evolution, to resolve the particle impoverishment under a proper size of particle population. We apply our proposed method to endoscope tracking for estimating three-dimensional motion of the endoscopic camera. The experimental results demonstrate that our proposed method offers more robust and accurate tracking than previous methods. The current tracking smoothness and error were significantly reduced from (3.7, 4.8) to (2.3 mm, 3.2 mm), which approximates the clinical requirement of 3.0 mm.
Wan, Y., Wu, Q. & He, X.S. 2014, 'Dense feature correspondence for video-based endoscope three-dimensional motion tracking', 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE-EMBS International Conference on Biomedical and Health Informatics, IEEE, Valencia, pp. 49-52.
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This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm.
Hasan, M.A., Xu, M. & He, S. 2011, 'A Comprehensive Approach to Automatic Image, Browsing for Small Display Devices', The Era of Interactive Media - Pacific-Rim Conference on Multimedia, Pacific-Rim Conference on Multimedia, Springer New York, Sydney, Australia, pp. 267-276.
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Recently, small displays are widely used to browse digital images. While using a small display device, the content of the image appears very small. Users have to use manual zooming and panning in order to see the detail of the image on a small display. Hence, an automatic image browsing solution is desired for user convenience. In this chapter, a novel comprehensive and efficient system is proposed to browse high resolution images using small display devices by automatically panning and zooming on Region-of-Interests (ROIs). The challenge is to provide a better user experience on heterogeneous small display sizes. First of all, an input image is classified into one of the three different classes: close-up, landscape and other. Then the ROIs of image are extracted. Finally, ROIs are browsed based on different intuitive and study based strategies. Our proposed system is evaluated by subjective test. Experimental results indicate that the proposed system is an effective large image displaying technique on small display devices.
Zeng, C., Jia, W. & He, S. 2013, 'Text Detection In Born-Digital Images Using Multiple Layer Images', 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, IEEE, Vancouver Canada, pp. 1947-1951.
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In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.
Wang, W., Wu, Q., He, S. & Yang, J. 2013, 'Generalized Local N-ary Patterns for Texture Classification', 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE International Conference on Advanced Video and Signal-Based Surveillance, IEEE, Krakow, Poland, pp. 324-329.
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Local Binary Pattern (LBP) has been well recognised and widely used in various texture analysis applications of computer vision and image processing. It integrates properties of texture structural and statistical texture analysis. LBP is invariant to monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. In recent years, various improvements have been achieved based on LBP. One of extensive developments was replacing binary representation with ternary representation and proposed Local Ternary Pattern (LTP). This paper further generalises the local pattern representation by formulating it as a generalised weight problem of Bachet de Meziriac and proposes Local N-ary Pattern (LNP). The encouraging performance is achieved based on three benchmark datasets when compared with its predecessors
Jan, M.A., Nanda, P., He, S. & Liu, R. 2013, 'Enhancing Lifetime and Quality of Data in Cluster-based Hierarchical Routing Protocol for Wireless Sensor Network', 2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, 2013 IEEE International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, CPS, Zhangjiajie, Hunan Province, P.R. China, pp. 1400-1407.
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Wireless Sensor Network (WSN) performs energyextensive tasks and it is essential to rotate sensor nodes frequently so that Cluster Head selections can be made efficiently. In this paper, we aim to improve the lifetime of sensor network by using LEACH based protocols and efficiently utilizing the limited energy available in these sensor nodes. In sensor network, the amount of data delivered at the base station is not important but it is the quality of the data which is of utmost importance. Our proposed approach significantly improves the life time and quality of data being delivered at the base station in sensor network. We evaluate our proposed approach using different sets of node energy levels and in each case our approach shows significant improvement over existing cluster-based hierarchical routing protocols. We evaluate our scheme in terms of energy consumption, life time and quality of data delivered at the base station.
Hasan, M., Xu, M., He, S. & Chen, L. 2012, 'Shot Classification Using Domain Specific Features for Movie Management', Lecture Notes in Computer Science, Springer, Busan, South Korea, pp. 314-318.
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Among many video types, movie content indexing and retrieval is a significantly challenging task because of the wide variety of shooting techniques and the broad range of genres. A movie consists of a series of video shots. Managing a movie at shot level provides a feasible way for movie understanding and summarization. Consequently, an effective shot classification is greatly desired for advanced movie management. In this demo, we explore novel domain specific features for effective shot classification. Experimental results show that the proposed method classifies movie shots from wide range of movie genres with improved accuracy compared to existing work
Mujtaba, M., Nanda, P. & He, S. 2012, 'Border Gateway Protocol Anomaly Detection using Failure Quality Control Method', 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, CPS (Conference Publishing Services), Liverpool UK, pp. 1239-1244.
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Border Gateway Protocol (BGP) is the de-facto inter-domain routing protocol used across thousands of Autonomous Systems (AS) joined together in the Internet. Security has been a major issue for BGP. Nevertheless, BGP suffers from serious threats even today, like Denial of Service (DoS) attack and misconfiguration of routing information. BGP is one of the complex routing protocols and hard to configure against malicious attacks. However, it is important to detect such malicious activities in a network, which could otherwise cause problems for availability of services in the Internet. In this paper we use the Failure Quality Control (FQC), a technique to detect anomaly packets in the network for real time intrusion detection.
Tan, T., Jamdagni, A., Nanda, P., He, S. & Liu, R. 2012, 'Evaluation on Multivariate Correlation Analysis Based Denial-of-Service Attack Detection System', 1st International Conference on Security of Internet of Things, 1st International Conference on Security of Internet of Things, ACM, India, pp. 1-5.
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In this paper, a Denial-of-Service (DoS) attack detection system is explored, where a multivariate correlation analysis technique based on Euclidean distance is applied for network traffic characterization and the principal of anomaly-based detection is employed in attack recognition. The effectiveness of the detection system is evaluated on the KDD Cup 99 dataset and the influence of data normalization on the performance of attack detection is analyzed in this paper as well. The evaluation results and comparisons prove that the detection system is effective in distinguishing DoS attack network traffic from legitimate network traffic and outperforms two state-of-the-art systems.
Zhang, Z. & He, S. 2013, 'Face Recognition Based on Modified LBP', The 7th International Conference on Computer Science and Education (ICCSE 2012), The 7th International Conference on Computer Science and Education (ICCSE 2012), IEEE Computer Society, Melbourne, Australia, pp. 160-164.
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Face recognition in unconstrained, natural conditions still remains a challenging task. As a powerful local descriptor, Local Binary Patterns has shown the advantage of representation and performance. However, it is still affected by robustness and accuracy. In this paper, a novel method is presented to improve the performance of automatic face recognition under uncontrolled conditions. We modify the conventional Local Binary Pattern and use it as a new feature descriptor. Partial Hausdorff Distance is applied as a dissimilarity measurement. Experimental results show that the proposed algorithm outperforms the traditional LBP approach in terms of accuracy rate and robustness. It can reduce the sensitivity caused by illumination variation, pose variation, occlusion etc.
Yeh, W., Yeh, Y., Chou, C., Chung, Y.Y. & He, S. 2012, 'A radio frequency identification network design methodology for the decision problem in Mackay Memorial Hospital based on swarm optimization', 2012 IEEE Congress on Evolutionary Computation (CEC), IEEE Congress on Evolutionary Computation (CEC), IEEE Computer Society, Brisbane Australia, pp. 1-7.
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Radio-frequency identification (RFID) is an automatic identification system which has become a hot topic in the fields of manufacturing, logistics, and so on. The purpose of this research is to propose a methodology for designing the RFID network planning problem (RNP) for application in the Mackay Memorial Hospital in Hsinchu, Taiwan. In this study, the RFID network is first considered as a grid and divided into several small squares. A soft computing methodology called FKB-SSO is proposed to solve the RNP problem based on simplified swarm optimization (SSO) by integrating k-means, fuzzy adaptive resonance theory (fuzzy-ART), and binary search. The proposed FKB-SSO will provide the basis for strategic decisions in constructing the RFID network to reduce the number of RFID readers with a minimal budget under the constraint of 100% coverage rate. The proposed FKB-SSO is more efficient than PSO and experts' manual solution in both run time and solution quality.
Du, R., Wu, Q. & He, S. 2012, 'Object Categorization Based on a Supervised Mean Shift Algorithm', Computer Vision- ECCV 2012 Workshops and Demonstrations, Springer-Verlag Berlin Heidelberg, Florence, Italy, pp. 611-614.
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In this work, we present a C++ implementation of object categorization with the bag-of-word (BoW) framework. Unlike typical BoW models which consider the whole area of an image as the region of interest (ROI) for visual codebook generation, our implementation only considers the regions of target objects as ROIs and the unrelated backgrounds will be excluded for generating codebook. This is achieved by a supervised mean shift algorithm. Our work is on the benchmark SIVAL dataset and utilizes a Maximum Margin Supervised Topic Model for classification. The final performance of our work is quite encouraging
Wang, W., Wu, Q., He, S. & Xu, M. 2012, 'On Splitting Dataset: Boosting Locally Adaptive Regression Kernels for Car Localization', 2012 12th International Conference on Control, Automation, Robotics & Vision, International Conference on Control, Automation, Robotics & Vision, IEEE Press, Guangzhou, China (People's Republic of), pp. 1154-1159.
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In this paper, we study the impact of learning an Adaboost classifier with small sample set (i.e., with fewer training examples). In particular, we make use of car localization as an underlying application, because car localization can be widely used to various real world applications. In order to evaluate the performance of Adaboost learning with a few examples, we simply apply Adaboost learning to a recently proposed feature descriptor - Locally Adaptive Regression Kernel (LARK). As a type of state-of-the-art feature descriptor, LARK is robust against illumination changes and noises. More importantly, we use LARK because its spatial property is also favorable for our purpose (i.e., each patch in the LARK descriptor corresponds to one unique pixel in the original image). In addition to learning a detector from the entire training dataset, we also split the original training dataset into several sub-groups and then we train one detector for each sub-group. We compare those features associated using the detector of each sub-group with that of the detector learnt with the entire training dataset and propose improvements based on the comparison results. Our experimental results indicate that the Adaboost learning is only successful on a small dataset when those learnt features simultaneously satisfy two conditions that: 1. features are learnt from the Region of Interest (ROI), and 2. features are sufficiently far away from each other.
Yu, D., Nanda, P. & He, S. 2012, 'Performance Uncertainty Impact on WSNs Design Evaluation', 2012 International Conference on Control Engineering and Communication Technology, 2012 International Conference on Control Engineering and Communication Technology, IEEE Computer Society, Shenyang China, pp. 723-726.
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In this paper we try to characterize wireless sensor network (WSNs) performance uncertainty (PU) attribute, identify the source and cause of PU, then we challenge that performance stability should treated seriously as one metric among other important metric depending application scenario. We further classify PU impacts on system evaluation and comparison process. Finally, we propose PU mitigation strategy
Tan, T., Jamdagni, A., He, S., Nanda, P. & Liu, R. 2012, 'Triangle-Area-Based Multivariate Correlation Analysis for Effective Denial-of-Service Attack Detection', 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Computer Society, Liverpool UK, pp. 33-40.
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Cloud computing plays an important role in current converged networks. It brings convenience of accessing services and information to users regardless of location and time. However, there are some critical security issues residing in cloud computing, such as availability of services. Denial of service occurring on cloud computing has even more serious impact on the Internet. Therefore, this paper studies the techniques for detecting Denial-of-Service (DoS) attacks to network services and proposes an effective system for DoS attack detection. The proposed system applies the idea of Multivariate Correlation Analysis (MCA) to network traffic characterization and employs the principal of anomaly-based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle area technique is proposed to enhance and speed up the process of MCA. The effectiveness of our proposed detection system is evaluated on the KDD Cup 99 dataset, and the influence of both non-normalized and normalized data on the performance of the detection system is examined. The results presented in the system evaluation section illustrate that our DoS attack detection system outperforms two state-of-theart approaches
Myint, H., Nanda, P. & He, S. 2012, 'Evaluation of billing and charging architecture for the Internet service provisioning', 2012 International Symposium on Communications and Information Technologies (ISCIT), 2012 International Symposium on Communications and Information Technologies (ISCIT), IEEE Computer Society, Goldcoast Australia, pp. 900-905.
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This article develops a charging scheme that is simple and easily usable for the users and provides them with the incentives to use only the resources they require. Our scheme has been developed and based on the use of Internet resource and demonstrates how the contributing providers can share the total charge earned by each mobile and wireless services in a fair way. We made a comparison of our architecture with existing architectures and demonstrated that our architecture adopts an accommodating approach for customer which is economically viable for the ISP provider.
Mudugamuwa, D.J., He, S. & Jia, W. 2012, 'Battle-Lemarie Wavelet Pyramid for Improved GSM Image Denoising', The 21st International Conference on Pattern Recognition (ICPR 2012), ICPR2012, IEEE, Tsukuba, Japan, pp. 3156-3159.
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Removing noise from a digital image is a challenging problem. Application of Gaussian Scale Mixtures (GSM) in the wavelet domain has been reported to be one of the most effective denoising algorithms, published to date. The performance of this algorithm depends on the chosen wavelet representation. In this paper, we introduce an improved wavelet pyramid representation based on the Battle-Lemarie wavelet which favors the GSM denoising performance. We present the experimental denoising results using the proposed pyramid representation, and they outperform state-of-the-art GSM denoising results reported in the literature.
Mudugamuwa, D.J., He, S. & Jia, W. 2012, 'Efficient Super-Resolution by Finer Sub-Pixel Motion Prediction and Bilateral Filtering', 2012 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Melbourne, Australia, pp. 800-805.
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Zhou, L., Qiao, Y., Yang, J. & He, S. 2013, 'Learning geodesic CRF model for image segmentation', 2012 IEEE International Conference on Image Processing, 2012 IEEE International Conference on Image Processing, IEEE Computer Society, Orlando, Florida USA, pp. 1565-1568.
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Graph cut based on color model is sensitive to statistical information of images. Integrating priority information into graph cut approach, such as the geodesic distance information, may overcome the well-known drawback of bias towards shorter paths that occurred frequently with graph cut methods. In this paper, a conditional random field (CRF) model is formulated to combine color model and geodesic distance information into a graph cut optimization framework. A discriminative model is used to capture more comprehensive statistical information for geodesic distance. A simple and efficient parameter learning scheme based on feature fusion is proposed for CRF model construction. The method is evaluated by applying it to segmentation of natural images, medical images and low contrast images. The experimental results show that the geodesic information obtained by learning can provide more reliable object features. The dynamic parameter learning scheme is able to select best cues from geodesic map and color model for image segmentation.
Du, R., Wu, Q., He, S. & Yang, J. 2012, 'Multi-Instance Learning with an Extended Kernel Density Estimation for Object Categorization', 2012 IEEE International Conference onMultimedia and Expo Workshops (ICMEW), IEEE International Conference onMultimedia and Expo Workshops, IEEE, Melbourne, Australia, pp. 477-482.
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Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended kernel density estimation (eKDE) which is an alternative to previous diverse density estimation (DDE). A fast method is devised to approximately locate the multiple modes of eKDE. Comparing to DDE, eKDE is more efficient and robust to the labeling noise (the mislabeled training data). We compare our approach with other state-of-the-art MIL methods in object categorization on the popular Caltech-4 and SIVAL datasets, the results illustrate that our approach provides superior performance.
Gong, C., Liu, Y., Li, T., Yang, J. & He, S. 2012, 'The Extended Co-learning Framework for Robust Object Tracking', 2012 IEEE International Conference on Multimedia and Expo (ICME), IEEE International Conference on Multimedia and Expo (ICME), IEEE, Melbourne, Australia, pp. 398-403.
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Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised learning, among three classifiers. Different from [1], random fern classifier is incorporated to deal with 2bitBP feature newly added and certain constraints are specially implemented in our framework. Besides, the way for selecting positive samples is also altered by us in order to achieve more stable tracking. Algorithm proposed in this paper is validated by tracking pedestrian and cup under occlusion. Experiments and comparison show that our algorithm can avoid drifting problem to some degree and make tracking result more robust and adaptive
Ambu Saidi, M.A., Lu, L., Tan, T., He, S., Jamdagni, A. & Nanda, P. 2012, 'A Nonlinear Correlation Measure for Intrusion Detection', The 7th International Conference on Frontier of Computer Science and Technology (FCST-12), IEEE Computer Society, Suzhou, China, pp. 1-7.
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The popularity of the Internet supplies attackers with a new means to violate any organizations and individuals. This raises the concerns of the Internet users and research community. One of the effective solutions of addressing this issue is Intrusion Detection System (IDS), which is defined as a type of security tools used to detect any malicious behaviors on computer networks. However, IDSs are commonly prone to high false positive rates. In order to solve this technical challenge, this paper proposes an effective Nonlinear Correlation Coefficient (NCC) based measure, which can accurately extract both linear and nonlinear correlations between network traffic records, for intrusion detection. Then, we demonstrate the effectiveness of our proposed NCC-based measure in extracting correlations by comparing against the Pearsonâs Correlation Coefficient (PCC) based measure. The demonstration is conducted on KDD Cup 99 data set, and the experimental results show that our proposed NCC-based measure not only helps reduce false alarm rate, but also helps distinguish normal and abnormal behaviors efficiently.
Wong, M., He, X., Nguyen, H. & Yeh, W. 2012, 'Particle Swarm Optimization Based Feature Selection in Mammogram Mass Classification', Proceedings of 2012 International Conference on Computerized Healthcare, 2012 International Conference on Computerized Healthcare, IEEE, Hong Kong, pp. 152-157.
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Mammography is currently the most effective method for early detection of breast cancer. This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal tissue regions by first finding the significant texture features of ROI using binary particle swarm optimization (BPSO). The data set used consisted of sixty-nine ROIs from the MIAS Mini-Mammographic database. Eighteen texture features were derived from the gray level co-occurrence matrix (GLCM) of each ROI. Significant features are found by a feature selection technique based on BPSO. The decision tree classifier is then used to classify the test set using these significant features. Experimental results show that the significant texture features found by the BPSO based feature selection technique can have better classification accuracy when compared to the full set of features. The BPSO feature selection technique also has similar or better performance in classification accuracy when compared to other widely used existing techniques
He, X. & Min, G. 2012, 'Message from the ISACC 2012 symposium chairs', Proceedings - 2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012, p. xxix.
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Chomsiri, T., He, S. & Nanda, P. 2012, 'Limitation of Listed-Rule Firewall and the Design of Tree-Rule Firewall', Lecture Notes in Computer Science, Springer, pp. 275-287.
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This research will illustrate that firewalls today (Listed-Rule Firewall) have five important limitations which may lead to security problem, speed problem and "difficult to use" problem. These limitations consist of, firstly, limitation about "Shadowed rules" (the rule that cannot match with any packet because a packet will be matched with other rules above) which can lead to security and speed problem. Secondly, limitatin about swapping position between rules can bring a change in firewall policy and cause security problem. The third limitation is about "Redundant rules" which can cause speed problem. Next,limitation of rule design; firewall administrators have to put "Bigger Rules" only at the bottom or lower positions can result in a "difficult to use" problem. Lastly, limitation from sequential computation can lead to speed problem. Moreover, we also propose design of the new firewall named "Tree-Rule Firewall" which does not have above limitations.
Tan, Z., Jamdagni, A., Nanda, P., He, X. & Liu, R.P. 2012, 'Evaluation on multivariate correlation analysis based denial-of-service attack detection system', ACM International Conference Proceeding Series, pp. 160-164.
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In this paper, a Denial-of-Service (DoS) attack detection system is explored, where a multivariate correlation analysis technique based on Euclidean distance is applied for network traffic characterization and the principal of anomaly-based detection is employed in attack recognition. The effectiveness of the detection system is evaluated on the KDD Cup 99 dataset and the influence of data normalization on the performance of attack detection is analyzed in this paper as well. The evaluation results and comparisons prove that the detection system is effective in distinguishing DoS attack network traffic from legitimate network traffic and outperforms two state-of-the-art systems. Copyright 2012 ACM.
Myint, H., Nanda, P. & He, X. 2012, 'Evaluation of billing and charging architecture for the Internet service provisioning', 2012 International Symposium on Communications and Information Technologies, ISCIT 2012, pp. 895-900.
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This article develops a charging scheme that is simple and easily usable for the users and provides them with the incentives to use only the resources they require. Our scheme has been developed and based on the use of Internet resource and demonstrates how the contributing providers can share the total charge earned by each mobile and wireless services in a fair way. We made a comparison of our architecture with existing architectures and demonstrated that our architecture adopts an accommodating approach for customer which is economically viable for the ISP provider. © 2012 IEEE.
Mareels, I., Yuan, K., He, X., Zhang, F., Oboler, A., Li, M., Yong, J. & Xi, B. 2012, 'Welcome to ICCSE', ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education.
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Wong, M., He, S. & Yeh, W. 2011, 'Image Clustering Using Particle Swarm Optimization', 2011 IEEE Congress on Evolutionary Computation (CEC), IEEE Congress on Evolutionary Computation, 2011 IEEE Congress of Evolutionary Computation (CEC 2011), New Orleans LA, pp. 1-7.
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This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation.
Wang, W., Wu, Q., Jia, W., He, S. & Yang, J. 2011, 'Learning Global and Local Features for License Plate Detection', Neural Information Processing, International Conference on Neural Information Processing, Springer-Verlag, Shanghai / China, pp. 547-556.
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This paper proposes an intelligent system that is capable of automiatically detecting license plates from static images captured by a digital still camera. A supervised learning apporach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is contructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning apporach and the slelected dSIFT features will be used to construct a stong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing work and promising results are obtained.
Mudugamuwa, D.J., Jia, W., He, S. & Yang, J. 2011, 'An Overcomplete Pyramid Representation for Improved GSM Image Denoising', Multimedia and Expo (ICME), 2011 IEEE International Conference on, IEEE International Conference on Multimedia and Expo, IEEE Computer Society, Barcelona Spain, pp. 1-6.
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Removing noise from a digital image is a challenging problem. Application of Gaussian Scale Mixtures (GSM) in the wavelet domain has been reported to be one of the most effective denoising algorithms, published to date. In this paper we investigate the impact of overcomplete wavelet image representations on the GSM image denoising algorithm. We explore the desirable local characteristics of wavelet coefficients that can enhance the efficiency of GSM denoising and based on the findings, we devise an improved over-complete pyramid representation to enhance the GSM denoising performance. We present the experimental denoising results using the proposed pyramid representation, and they outperform state-of-the-art GSM denoising results reported in the literature.
Wang, W., Wu, Q., Jia, W. & He, S. 2011, 'Training-Free License Plate Detection Using Vehicle Symmetry and Simple Features', Proceedings: Twenty-sixth International Conference Image and Vision Computing New Zealand, Image and Vision Computing New Zealand 2011 IVCNZ, Image and Vision Computing New Zealand, Auckland, New Zealand, pp. 260-265.
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In this paper, we propose a training free license plate detection method. We use a challenging benchmark dataset for license plate detection. Unlike many existing approaches, the proposed approach is a training free method, which does not require supervised training procedure and yet can achieve a reasonably good performance. Our motivation comes from the fact that, although license plates are largely variant in color, size, aspect ratio, illumination condition and so on, the rear view of vehicles is mostly symmetric with regard to the vehicles central axis. In addition, license plates for most vehicles are usually located on or close to the vertical axis of the vehicle body along which the vehicle is nearly symmetric. Taking advantage of such prior knowledge, the license plate detection problem is made simpler compared to the conventional scanning window approach which not only requires a large number of scanning window locations, but also requires different parameter settings such as scanning window sizes, aspect ratios and so on.
Wang, W., Wu, Q., He, S. & Jia, W. 2011, 'More on Weak Feature: Self-correlate Histogram Distances', Advances in Image and Video Technology, Pacific-Rim Symposium on Image and Video Technology, Springer-Verlag, Gwangju, South Korea, pp. 214-223.
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Tan, T., Jamdagni, A., He, S., Nanda, P. & Liu, R. 2011, 'Multivariate Correlation Analysis Technique Based on Euclidean Distance Map for Network Traffic Characterization', Information and Communications Security 13th International Conference, ICICS 2011, Springer Verlag, Beijing/China, pp. 388-398.
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The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches have been conducted and attempted to overcome this problem, there are some constraints in these works. In this paper, we propose a technique based on Euclidean Distance Map (EDM) for optimal feature extraction. The proposed technique runs analysis on original feature space (first-order statistics) and extracts the multivariate correlations between the first-order statistics. The extracted multivariate correlations, namely second-order statistics, preserve significant discriminative information for accurate characterizations of network traffic records, and these multivariate correlations can be the high-quality potential features for DoS attack detection. The effectiveness of the proposed technique is evaluated using KDD CUP 99 dataset and experimental analysis shows encouraging results.
Wang, L., He, S., Du, R., Jia, W., Wu, Q. & Yeh, W. 2011, 'Facial Expression Recognition on Hexagonal Structure Using LBP-Based Histogram Variances', Advances in Multimedia Modeling - Proceedings of the 17th International Multimedia Modeling Conference, MMM 2011, International Multimedia Modeling Conference, Springer, Taipei, Taiwan, pp. 35-45.
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In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for facial expression. We take into account the human perspective and understanding of facial expressions. For the first time, we propose to use the Local Binary Pattern (LBP) defined on the hexagonal structure to extract local, dynamic facial features from facial expression images. The dynamic LBP features are used to construct a static image, namely Hexagonal Histogram Variance Face (HHVF), for the video representing a facial expression. We show that the HHVFs representing the same facial expression (e.g., surprise, happy and sadness etc.) are similar no matter if the performers and frame rates are different. Therefore, the proposed facial recognition approach can be utilised for the dynamic expression recognition. We have tested our approach on the well-known Cohn-Kanade AU-Coded Facial Expression database. We have found the improved accuracy of HHVF-based classification compared with the HVF-based approach
chen, X., He, S., Yang, J. & Wu, Q. 2011, 'An Effective Document Image Deblurring Algorithm', Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Colorado Springs, pp. 369-376.
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Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method.
Tan, T., Jamdagni, A., He, S., Nanda, P. & Liu, R. 2011, 'Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis', Neural Information Processing 18th International Conference, ICONIP 2011, International Conference on Neural Information Processing, Springer-Verlag, Shanghai, China, pp. 756-765.
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The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks.
Zheng, L. & He, S. 2011, 'Character Segmentationfor License Plate Recognition by K-Means algorithm', Image Analysis and Processing - ICIAP2011, Springer-Verlag Berlin / Heidelberg, Ravenna, Italy, pp. 444-453.
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Abstract. In this paper an improved K-means algorithm is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of different image segmentation approaches, the K-means algorithm based method gave better image segmentation results. The K-means algorithm was modified by introducing automatic cluster number determination by filtering SIFT key points. After modification it efficiently detects the local maxima that represent different clusters in the image. The process is successful by getting a clean license plate image. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate. The recognition rate increased from about 86.6% before our proposed process to about 94.03% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved.
Xu, M., He, S., Xu, C., Wang, J., Hasan, M.A., Lu, H. & Jin, J.S. 2011, 'Using Context Saliency for Movie Shot Classification', 18th IEEE International Conference on Image Processing, 18th IEEE International Conference on Image Processing, IEEE Computer Society, Brussels, Belgium, pp. 3653-3656.
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Movie shot classification is vital but challenging task due to various movie genres, different movie shooting techniques and much more shot types than other video domain. Variety of shot types are used in movies in order to attract audiences attention and enhance their watching experience. In this paper, we introduce context saliency to measure visual attention distributed in keyframes for movie shot classification. Different from traditional saliency maps, context saliency map is generated by removing redundancy from contrast saliency and incorporating geometry constrains. Context saliency is later combined with color and texture features to generate feature vectors. Support Vector Machine (SVM) is used to classify keyframes into pre-defined shot classes. Different from the existing works of either performing in a certain movie genre or classifying movie shot into limited directing semantic classes, the proposed method has three unique features: 1) context saliency significantly improves movie shot classification; 2) our method works for all movie genres; 3) our method deals with the most common types of video shots in movies. The experimental results indicate that the proposed method is effective and efficient for movie shot classification.
Wong, M.T., He, X. & Yeh, W.C. 2011, 'Image clustering using Particle Swarm Optimization', 2011 IEEE Congress of Evolutionary Computation, CEC 2011, pp. 262-268.
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This paper proposes an image clustering algorithm using Particle Swarm Optimization (PSO) with two improved fitness functions. The PSO clustering algorithm can be used to find centroids of a user specified number of clusters. Two new fitness functions are proposed in this paper. The PSO-based image clustering algorithm with the proposed fitness functions is compared to the K-means clustering. Experimental results show that the PSO-based image clustering approach, using the improved fitness functions, can perform better than K-means by generating more compact clusters and larger inter-cluster separation. © 2011 IEEE.
Wang, W., Du, R., Wu, Q. & He, S. 2010, 'Adaptive Stick-Like Features for Human Detection Based on Multi-scale Feature Fusion Scheme', Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), Digital Image Computing: Techniques and Applications, IEEE Computer Society, Sydney, Australia, pp. 375-380.
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Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database.
Jamdagni, A., Tan, T., Nanda, P., He, S. & Liu, R. 2010, 'Intrusion Detection Using GSAD Model for HTTP Traffic on Web Services', 2010 IWCMC - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, International Wireless Communications and Mobile Computing Conference, Association for Computing Machinery, Inc. (ACM), Caen, France, pp. 1193-1197.
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Intrusion detection systems are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. Hypertext Transport Protocol (HTTP) is used for new applications without much interference. In this paper, we focus on intrusion detection of HTTP traffic by applying pattern recognition techniques using our Geometrical Structure Anomaly Detection (GSAD) model. Experimental results reveal that features extracted from HTTP request using GSAD model can be used to distinguish anomalous traffic from normal traffic, and attacks carried out over HTTP traffic can be identified. We evaluate and compare our results with the results of PAYL intrusion detection systems for the test of DARPA 1999 IDS data set. The results show GSAD has high detection rates and low false positive rates.
Zheng, L., He, S., Samali, B. & Yang, L. 2010, 'Accuracy Enhancement for License Plate Recognition', Proceedings - 10th IEEE International Conference on Computer and Information Technology (CIT 2010), IEEE International Conference on Computer and Information Technology, IEEE Computer Society, Bradford, West Yorkshire UK, pp. 511-516.
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Automatic License Plate Recognition is useful for real time traffice management and surveillance. License plate recognition usually contains two steps, namely license plate detection/localization and character recognition. Recognizing character in a license plate is very difficult task due to poor illumination conditions and rapid motion of vehicles. When using an OCR for character recognition, it is crucial to correctly remove the license plate boundaries after the step for license plate detection. No matter which OCRs are used, the recognition accuracy will be significantly reduced if the boundaries are not properly removed. This paper presents an efficient algorithm for non character area removal. The algorithm is based on the license plates detected using an AdaBoost algorithm. Then it follows the steps of character height estimation, character width estimation, segmentation and block identification. The algorithm is efficient and can be applied in real time applications. The experiments are performed using OCR software for character recognition. It is shown that much higher recognition accuracy is obtained by gradually removing the license plate boundaries
Jia, W., He, S. & Wu, Q. 2010, 'ECCH: A Novel Color Coocurrence Histogram', Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference, Acoustics Speech and Signal Processing, IEEE Computer Society, Dallas, USA, pp. 1258-1261.
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In this paper, a novel color coocurrence histogram method, named eCCH which stands for color cooccurence histogram at edge points, is proposed to describe the spatial-color joint distribution of images. Unlike all existing ideas, we only investigate the color distribution of pixels located at the two sides of edge points on gradient direction lines. When measuring the similarity of two eCCHs, the Gaussian weighted histogram intersection method is adopted, where both identical and similar color pairs are considered to compensate color variations. Comparative experimental results demonstrate the performance of the proposed eCCH in terms of robustness to color variance and small computational complexity.
Song, J., He, S. & Qian, F. 2010, 'Study on Generalized Fractal Algorithm of Global Optimization', Proceedings: The 2nd IEEE International Conference on Advanced Computer - Vol. 5, IEEE International Conference on Advanced Computer Control, Institute of Electronics Engineers, Inc., Shenyang, China, pp. 286-290.
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To promote a fractal algorithm from being suitable for global optimization only on three dimensional spaces, this paper presents a simple and convenient method for dividing an n-dimensional hypercube. A key problem is then solved to develop the fractal algorithm in a high dimensional space so that the fractal algorithm becomes a generalized global optimization algorithm. The theoritical foundation of the algorithm is set up. Simulations show the generalized fractal algorithm is effective.
Jamdagni, A., Tan, T., Nanda, P., He, S. & Liu, R. 2010, 'Mahalanobis Distance Map Approach for Anomaly Detection of Web-Based Attacks', The Proceedings of the 8th Australian Information Security Management Conference, Australian Information Security Management Conference, SECAU - Security Research Centre, Perth, Western Australia, pp. 8-17.
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Web servers and web-based applications are commonly used as attack targets. The main issues ar how to prevent unauthorised access and to protect web servers from the attack. Intrusion Detection Systems (IDSs) are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. In this paper, we focus on the detection of various web-based attacks using Geometrical Structure Anomaly Detection (GSAD) model and we also propose a novel algorithm for the selection of most discriminating features to improve the computational complexity of payload-based GSAD model. Linear Discriminant method (LDA) is used for the feature reduction and classification of the incoming network traffic. GSAD model is based on a pattern recognition technique used in image processing. It analyses te correlations between various payload fetures and uses Mahalanobis Distance Map (MDM) to calculate the difference between normal and abnormal network traffic. We focus on the detection of generic attacks, shell code attacks, polymorphic attacks and polymorphic blending attacks. We evaluate accuracy of GSAD model experimentally on the real world attacks dataset created at Georgia Institute of Technology. We conducted preliminary experiments on the DARPA 99 dataset to evaluate the accuracy of feature reduction.
Mudugamuwa, D.J., Jia, W. & He, S. 2010, 'Asymmetric, Non-unimodal Kernel Regression for Image Processing', Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), Digital Image Computing: Techniques and Applications, IEEE Computer Society, Sydney, Australia, pp. 141-145.
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Kernel regression has been previously proposed as a robust estimator for a wide range of image processing tasks, including image denoising, interpolation and superresolution. In this article we propose a kernel formulation that relaxes the usual symmetric and unimodal properties to effectively exploit the smoothness characteristics of natural images. The proposed method extends the kernel support along similar image characteristics to further increase the robustness of the estimates. Application of the proposed method to image denoising yields significant improvement over the previously reported regression methods and produces results comparable to the state-ofthe-art denoising techniques.
Tan, T., Jamdagni, A., He, S., Nanda, P., Liu, R., Jia, W. & Yeh, W. 2010, 'A Two-Tier System for Web Attack Detection Using Linear Discriminant Method', Information and Communications Security - Lecture Notes in Computer Science 6476, Information and Communications Security, Springer, Barcelona, Spain, pp. 459-471.
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Computational cost is one of the major concerns of the commercial Intrusion Detection Systems (IDSs). Although these systems are proven to be promising in detecting network attacks, they need to check all the signatures to identify a suspicious attack in the worst case. This is time consuming. This paper proposes an efficient two-tier IDS, which applies a statistical signature approach and a Linear Discriminant Method (LDM) for the detection of various Web-based attacks. The two-tier system converts high-dimensional feature space into a low-dimensional feature space. It is able to reduce the computational cost and integrates groups of signatures into an identical signature. The integration of signatures reduces the cost of attack identification. The final decision is made on the integrated low-dimensional feature space. Finally, the proposed two-tier system is evaluated using DARPA 1999 IDS dataset for webbased attack detection.
Chen, X., Chung, Y.Y., Bae, C., He, S. & Yeh, W. 2010, 'Error Concealment for H.264/AVC using Regression Modelling', 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE), International Conference on Consumer Electronics, IEEE Computer Society, Las Vegas, USA, pp. 265-266.
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This paper presents a novel error concealment algorithm for H.264/AVC, using a regression model constructed according to the spatial relationship between the block locations and their local motion activities. The experimental results show that the proposed algorithm achieves significant PSNR improvement over the existing methods. Moreover, the implementation of the proposed algorithm is very simple and therefore it can be readily applied to video applications running on various consumer electronic devices, including mobile devices.
He, S., Wei, D., Lam, K.M., Li, J., Wang, L., Jia, W. & Wu, Q. 2010, 'Canny Edge Detection Using Bilateral Filter on Real Hexagonal Structure', 12th International Conference - Advanced Concepts for Intelligent Vision System, ACIVS 2010, Advanced Concepts for Intelligent Vision System, Springer-Verlag, Sydney, Australia, pp. 233-244.
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Edge detection plays an important role in image processing area. This paper presents a Canny edge detection method based on bilateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to prserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on sampled images represented on a real hexagon structure. Due to the compact and circular nature of hexagonal lattice, a better quality edge map is obtained on the hexagonal structure than common edge detection on square structure. Experimental results using proposed methods exhibit also the faster speed of detection on hexagonal structure.
Tan, T., Jamdagni, A., He, S. & Nanda, P. 2010, 'Network Intrusion Detection Based on LDA for Payload Feature Selection', IEEE Globecom 2010 Workshop on Web and Pervasive Security (WPS 2010), IEEE Globecom Workshop on Web and Pervasive Security, IEEE Computer Society, Miami USA, pp. 1590-1594.
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Anomaly Intrusion Detection System (IDS) is a statistical based network IDS which can detect attack variants and nove attacks without a priori knowledge. Current anomaly IDSs are inefficient for real-time detection because of their complex computation. This paper proposes a novel approach to reduce the heavy computational cost of an anomaly IDS. Linear Discriminant Analysis (LDA) and difference distance map are used for selection of significant features. This approach is able to transform high-dimensional features. This approach is able to transform high-dimensional feature vectors into a low-dimensional domain. The similarity between new incoming packets and a normal profile is determined using Euclidean distance o the simple, low dimensional feature domain. The final decision will be made according to a pre-calculated threshold to diffferentiate normal and abnormal network packets. The proposed approach is evaluated using DARPA 1999 IDS dataset.
Zheng, L., Gao, J. & He, S. 2010, 'Efficient Character Segmentation on Car License Plates', Proc. of the 11th. Int. Conf. Control, Automation, Robotics and Vision (ICARCV 2010), Int. Conf. Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 1139-1144.
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In this paper an improved hill climbing algorithm based method is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of two different types of image segmentation approaches, the hill climbing algorithm based method gave a better image segmentation results. The hill climbing algorithm was modified by introducing automatic parameter determination and smart searching. After modification it efficiently detects the peaks (local maxima) that represent different clusters in the global histogram of an image. The process is successful by getting a clean license plate image removing all unwanted areas. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate after non-character areas are removed. The recognition rate increased from about 30.6% before our proposed process to about 91.3% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved.
Du, R., Wang, W., Wu, Q. & He, S. 2010, 'Learn Concepts in Multiple-instance Learning with Diverse Density Framework Using Supervised Mean Shift', Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), Digital Image Computing: Techniques and Applications, IEEE Computer Society, Sydney, Australia, pp. 643-648.
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Many machine learning tasks can be achieved by using Multiple-instance learning (MIL) when the target features are ambiguous. As a general MIL framework, Diverse Density (DD) provides a way to learn those ambiguous features by maxmising the DD estimator, and the maximum of DD estimator is called a concept. However, modeling and finding multiple concepts is often difficult especially without prior knowledge of concept number, i.e., every positive bag may contain multiple coexistent and heterogeneous concepts but we do not know how many concepts exist. In this work, we present a new approach to find multiple concepts of DD by using an supervised mean shift algorithm. Unlike classic mean shift (an unsupervised clustering algorithm), our approach for the first time introduces the class label to feature point and each point differently contributes the mean shift iterations according to its label and position. A feature point derives from an MIL instance and takes corresponding bag label. Our supervised mean shift starts from positive points and converges to the local maxima that are close to the positive points and far away from the negative points. Experiments qualitatively indicate that our approach has better properties than other DD methods.
Xu, M., Chen, L., He, S., Xu, C. & Jin, J. 2010, 'Adaptive Local Hyperplanes for MTV affective analysis', Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, International Conference on Internet Multimedia Computing and Service, ACM, Harbin, China, pp. 167-170.
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Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying
Liu, J., Yang, J., Zhang, Y. & He, S. 2010, 'Action Recognition by Multiple Features and Hyper-sphere Multi-class SVM', Proceedings: 2010 20th International Conference Pattern Recognition (ICPR 2010), International Conference Pattern Recognition, IEEE Computer Society, Istanbul Turkey, pp. 3744-3747.
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In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single features based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids and 2-D SIFT), and ii) the higher order statistical models of interest points, which aims to capture the global information of the actor. We construct video presentation in terms of local space time features and global features and integrate such representations with hper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed approach is effective. An additional experiment shows that using both local and global features provides a richer representation of human action when compared to the use of a single feature type.
li, Y., Zhou, Y., Yan, J., Yang, J. & He, S. 2010, 'Tensor Error Correction for Corrupted Values in Visual Data', 2010 IEEE International Conference on Image Processing ICIP 2010 - Proceedings, IEEE International Conference on Image Processing, IEEE Computer Society, Hongkong, pp. 2321-2324.
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The multi channel image or the video clip has the natural form of tensor. The value of the tensor can be corrupted due to noise in acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X=L+S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to be tensor case by the definition of tensor trace norm in (6). Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
Jia, W., He, S. & Wu, Q. 2010, 'Segmenting Characters from License Plate Images with Little Prior Knowledge', Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), Digital Image Computing: Techniques and Applications, IEEE Computer Society, Sydney, Australia, pp. 220-226.
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In this paper, to enable a fast and robust system for automatically recognizing license plates with various appearances, new and simple but efficient algorithms are developed to segment characters from extracted license plate images. Our goal is to segment characters properly from a license plate image region. Different from existing methods for segmenting degraded machine-printed characters, our algorithms are based on very weak assumptions and use no prior knowledge about the format of the plates, in order for them to be applicable to wider applications. Experimental results demonstrate promising efficiency and flexibility of the proposed scheme.
Zeng, C., Jia, W., He, S. & Yang, J. 2010, 'Graph-based text segmentation using a selected channel image', Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), Digital Image Computing: Techniques and Applications, IEEE Computer Society, Sydney, Australia, pp. 535-539.
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This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a twopolarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.
Jamdagni, A., Tan, T., Liu, R., Nanda, P. & He, S. 2010, 'Pattern Recognition Approach for Anomaly Detection of Web-based Attacks', The Seventh Annual CSIRO ICT Centre Science and Engineering Conference, Annual CSIRO ICT Centre Science and Engineering Conference, CSIRO, Australian Technology Park, Eveleigh, NSW, Australia, pp. 1-2.
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The universal use of the Internet has made it more difficult to achieve high security. Attackers target web applications instead of Telnet ports. Cyber-attacks and breaches of information security are increasing in frequency. The goal of Intrusion Detection Systems (IDSs) is to monitor network traffic and detect web-based attacks. Common IDSs are either signature based or anomaly based. Signature based IDS is unable to detect novel attack (Le., zero-day) or polymorphic attacks, until the signature database is updated. On the other hand, an anomaly-based IDS can detect new attacks and polymorphic attacks. However, anomaly based system has a relatively high number of false positives.
Wang, F., Yang, J., He, X. & Loza, A. 2010, 'Surveillance video object tracking with differential ssim', International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
The recently proposed use of the structural similarity measure, in the particle filter-based video tracker has been shown to improve the tracking performance, compared to similar methods using the colour or edge histograms and Bhattacharyya distance. However, the combined use of the structural similarity and a particle filter results in a computationally complex tracker that may not be suitable for some real time applications. In this paper, a novel fast approach to the use of the structural similarity in video tracking is proposed. The tracking algorithm presented in this work determines the state of the target (location, size) based on the gradient ascent procedure applied to the structural similarity surface of the video frame, thus avoiding computationally expensive sampling of the state space. The new method, while being computationally less expensive, performs better, than the standard mean shift and the structural similarity particle filter trackers, as shown in exemplary surveillance video sequences.
Tan, T., He, S. & Nanda, P. 2009, 'Web Service Locating Unit in RFID-centric Anti-counterfeit System', Proceeding of 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE Computer Society, Chengdu, Sichuan, China, pp. 389-393.
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Web Service Locating Unit (WSLU) is able to simplify the deployment of RFID-centric anti-counterfeit system over the Internet.
Jamdagni, A., Tan, T., Nanda, P., He, S. & Liu, R. 2009, 'Intrusion Detection Using Geometrical Structure', Proceeding of 2009 International Conference on Frontier of Computer Science and Technology, International Conference on Frontier of Computer Science and Technology, IEEE Computer Society, Shanghai, China, pp. 327-333.
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Geometrical Structure Anomaly Detection (GSAD) model to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile.
Du, R., Wu, Q., He, S., Jia, W. & Wei, D. 2009, 'Facial Expression Recognition Using Histogram Variances Faces', Proceedings of the Ninth IEEE Computer Society Workshop on Application of Computer Vision (WACV 2009), IEEE Workshop on Applications of Computer Vision, IEEE, Snowbird, USA, pp. 341-347.
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In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances Face (HVF), by fusing histogram variances among the frames in a video. The HVFs can be automatically obtained from videos with different frame rates and immune to illumination interference. In our experiments, for the videos picturing the same facial expression, e.g., surprise, happy and sadness etc., their corresponding HVFs are similar, even though the performers and frame rates are different. Therefore the static facial recognition approaches can be utilised for the dynamic expression recognition. We have applied this approach on the well-known Cohn-Kanade AUCoded Facial Expression database then classified HVFs using PCA and Support Vector Machine (SVMs), and found the accuracy of HVFs classification is very encouraging.
Mudugamuwa, D., He, S., Wei, D. & Ahn, C. 2009, 'Super-Resolution by Prediction Based Sub-pel Motion Estimation', Proceedings of Image and Vision Computing New Zealand 2009 (IVCNZ2009), Image and Vision Computing Conference, IEEE, Wellington, New Zealand, pp. 282-287.
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Super-resolution reconstruction produces highresolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are usually very sensitive to their assumed models of data and noise, and also computationally expensive for many practical applications. In this paper we adopt computationally efficient prediction based sub-pel motion estimation to produce a fast super-resolution reconstruction that can also accommodate generic motion patterns. The proposed algorithm adaptively exploits the available high frequency content in adjacent video frames to generate high resolution video frames. Initial experiments showed promising results of around 2dB, PSNR improvement, over single frame bi-linear interpolation.
Luo, S., Hu, Q., He, S., Li, J., Jin, J. & Park, M. 2009, 'Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines', 2009 IEEE/ICME International Conference on Complex Medical Engineering, IEEE, Tempe, AZ, USA, pp. 1-5.
This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented to classify the data into pixel-wised liver area or non-liver area. Finally, integrated morphological operations are designed to remove noise and finally delineate the liver. Our unique contributions to liver segmentation are twofold: one is that it has been proved through experiments that wavelet features present good classification result when SVMs are used; the other is that the combination of morphological operations with the pixel-wised SVM classifier can delineate volumetric liver accurately. The algorithm can be used in an advanced computer-aided liver disease diagnosis and liver surgical planning system. Examples of applying the proposed algorithm on real CT data are presented with performance validation based on the comparison between the automatically segmented results and manually segmented ones.
He, S., Li, J., Wei, D., Jia, W. & Wu, Q. 2009, 'Canny edge detection on a virtual hexagonal image structure', 2009 Joint Conferences on Pervasive Computing (JCPC2009), IEEE, Taipei, Taiwan, pp. 167-172.
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Canny edge detector is the most popular tool for edge detection and has many applications in the areas of image processing, multimedia and computer vision. The Canny algorithm optimizes the edge detection through noise filtering using an optimal function approximated by the first derivative of a Gaussian. It identifies the edge points by computing the gradients of light intensity function based on the fact that the edge points likely appear where the gradient magnitudes are large. Hexagonal structure is an image structure alternative to traditional square image structure. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses linear interpolation described for conversion between square and hexagonal structures. Gaussian filtering together with gradient computation is performed on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before the thresholds of Canny algorithm are applied to determine the final edge map. The experimental results show the edge detection on hexagonal structure using static and video images, and the comparison with the results using Canny algorithm on square structure.
Jamdagni, A., Tan, T., Liu, R., Nanda, P. & He, S. 2009, 'A Frame Work for Geometrical Structure Anomaly Detection Model', The sixth annual CSIRO ICT Centre Science and Engineering Conference, Centre Science and Engineering Conference, CSIRO, Australian Technology Park, NSW, Australia, pp. 109-110.
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The growth of Internet offers quality and convenience to human life, but at the same time provides a platform for hackers and criminals. The Internet security hence becomes an important issue. Intrusion Detection System (IDS) is designed to detect intrusion and also to prevent a system from being compromised. In this paper, we present a novel Geometrical Structure Anomaly Detection (GSAD) model. GSAD employs pattern recognition techniques previously used in human detection [2}.
Mudugamuwa, D., He, S., Ahn, C. & Yang, J. 2009, 'Higher order prediction for sub-pixel motion estimation', IEEE 16th International Conference on Image Processing (ICIP2009), IEEE International Conference on Image Processing, IEEE, Cairo, Egypt, pp. 1585-1588.
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Estimating motion between two frames of a video sequence, up to sub-pixel accuracy, is a critical task for many image processing applications. Efficient block matching algorithms were proposed for motion estimation up to pixel accuracy. Applying these fast block search algorithms to up-sampled and interpolated frames can produce good results but with significant increase in computations. To reduce the number of search points, and therefore the computational cost, quadratic prediction was proposed earlier to predict the location of minimum block matching error, and then to limit the search window to the vicinity of the predicted location. In this paper we investigate the typical behavior of block matching error surface and propose an improved higher order prediction that models the error surface more accurately, utilizing additional local image behavior. Initial experiments have proved promising results of about 50% more improvement in PSNR compared to quadratic prediction with only a marginal increase in the computational cost
Ma, X., Pan, R., Wang, L. & He, S. 2009, 'A Method Based on Orientation Field for Skew Correction of License Plate', 2009 Second Asia-Pacific Conference on Computational Intelligence and Industrial Applications, Asia-Pacific Conference on Computational Intelligence and Industrial Applications, IEEE Industrial Electronics Society, Wuhan, China, pp. 308-311.
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Character recognitions are generally very sensitive to skew in the automatic license plate recognition (ALPR) system. The skew correction of license plate thus is an important step in ALPR. In this paper, we propose an orientation-based method for skew detection. The license plate image is firstly divided into a set of 5x5 non-overlapping blocks. The local orientation of each black is estimated by gradients of pixels in the block. Next, the direction histogram which can reveal the overall orientation information in the license plate image is counted. The skew angle of license plate is detected by the local maximum of the direction histogram. The experimental results demonstrate the great robustness and efficiency of our method.
Ye, Y., He, X., Li, J., Jia, W. & Wu, Q. 2009, 'Image transformation on hexagonal structure based on conversion between 1d and 2d coordinates', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 571-578.
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Spiral Architecture, a hexagonal image structure is a novel and powerful approach to machine vision system. The pixels on Spiral architecture are geometrically arranged using a 1D (Spiral) addressing scheme in an ascending order along a spiral-like curve. Spiral addition and Spiral multiplication are defined based on the Spiral addresses on Spiral Architecture. These two fundamental operations result in fast and easy translation, rotation and separation on images, and hence play very important roles for image processing on Spiral Architecture. Moreover, 2D coordinates according to rows and columns defined on Spiral Structure provide a good mapping to the ordinary 2D coordinates defined on the common square image structure. Therefore, how to convert the 1D Spiral addresses from and to the 2D coordinates on Spiral Architecture has become very important to apply the theory developed on a hexagonal image structure for image processing (e.g., rotation). In this paper, we perform a fast way to correctly locate any hexagonal pixel when its Spiral address is known, and compute the Spiral address of any hexagonal pixel when its location is known. As an illustration of the use of conversions, we demonstrate the accurate image translation and rotation using experimental results. © 2009 Springer Berlin Heidelberg.
Jiang, Y., Chen, Y., Zeng, Z. & He, S. 2008, 'A Bank Customers Credit Evaluation Based on the Decision Tree and the Simulated Annealing Algorithm', 8th IEEE International Conference on Computer and Information Technology (CIT2008), IEEE International Conference on Computer and Information Technology, IEEE Computer Society, Sydney, Australia, pp. 203-206.
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C4.5 is a learning algorithm that adopts local search strategy, and it cannot obtain the best decision rules. On the other hand, the simulated annealing algorithm is a globally optimized algorithm and it avoids the drawbacks of C4.5. This paper proposes a new credit evaluation method based on decision tree and simulated annealing algorithm. The experimental results demonstrate that the proposed method is effective.
Li, J. & He, S. 2008, 'Bi-Cubic Interpolation for Image Conversion from Virtual Hexagonal to Square Structure', 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV2008), International Conference on Image Processing, Computer Vision, and Pattern Recognition, CSREA Press, Las Vegas, USA, pp. 570-574.
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Du, C., Wu, Q., Yang, J., He, S. & Chen, Y. 2008, 'Subspace Analysis Methods plus Motion History Image for Human Action Recognition', Digital Image Computing: Techniques and Applications, 2008. DICTA '08., Digital Image Computing Techniques and Applications, IEEE, Canberra, Australia, pp. 606-611.
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This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method.
Wu, Q., Du, C., Yang, J., He, S. & Chen, Y. 2008, 'Pedestrian Detection Using Hybrid Statistical Feature', IEEE 10th Workshop on Multimedia Signal Processing, 2008, International Workshop on Multimedia Signal Processing, IEEE, Cairns, Queensland, Australia, pp. 101-106.
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A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA gait database and the additional non-human objects data.
Chen, Y., Wu, Q., He, S., Du, C. & Yang, J. 2008, 'Extracting key postures in a human action video sequence', IEEE 10th Workshop on Multimedia Signal Processing, 2008, International Workshop on Multimedia Signal Processing, IEEE, Cairns, Queensland, Australia, pp. 569-573.
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Human key posture extraction from videos will benefit video storage, video retrieval, human action recognition, human behaviour understanding and so on. This paper presents an approach to select key postures from human action sequences using 2D information. There are two steps in the proposed method. Information measurement which is a kind of global feature of a frame is used to roughly find key posture candidates. Then, a body skeleton feature which is a kind of local feature is applied to select final key postures from the candidates obtained in the first step. The experiments show that the proposed method is efficient.
Chen, Y., Wu, Q. & He, S. 2008, 'Using Dynamic Programming to Match Human Behavior Sequences', International Conference on Control, Automation, Robotics and Vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Hanoi, Vietnam, pp. 1498-1503.
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This paper proposed a new approach for recognition and matching the human behavior sequence. Each human behavior sequence is represented by its key postures to greatly reduce the computation time. Normalization is applied to all the behavior sequences key postures for matching. A dynamic time warping (DTW) algorithm is used to perform the alignment of two time series. Experiments are carried out on an open human behavior database and exciting results have been obtained.
El Shawi, R. & He, S. 2008, 'An Efficient Algorithm for Image Retrieval through Fusion of Two Clustering Approaches Based on Combined Features', Image and Vision Computing New Zealand (IVCNZ 2008), Image and Vision Computing Conference, IEEE, Christchurch, New Zealand, pp. 1-6.
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This paper proposes new approaches for image representation to bridge the gap between visual features and semantics. Two new combined feature extraction approaches are used to extract significant features from images. Each approach is a hybrid of two feature extraction methods and tries to capture both colour and texture information. In order to improve the query processing time and avoid the linear search problem, a clustering technique is applied on the image dataset according to each feature extraction approach. The clustering outcomes of the two feature extraction approaches are combined together using a decision fusion technique. The fused results show an improvement over any single approach. An implemented prototype system demonstrates a promising retrieval performance examined on 1000 colour images from CORAL dataset in comparison with a peer system in literature.
He, S., Li, J. & Chen, Y. 2008, 'Local Binary Patterns with Mahalanobis Distance Maps for Human Detection', International Congress on Image and Signal Processing (CISP2008), International Congress on Image and Signal Processing, IEEE Computer Society, Sanya, China, pp. 520-524.
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He, S., Zheng, L., Wu, Q., Jia, W., Samali, B. & Palaniswami, M.S. 2008, 'Segmentation of Characters on Car License Plates', IEEE 10th Workshop on Multimedia Signal Processing, 2008, International Workshop on Multimedia Signal Processing, IEEE, Cairns, Australia, pp. 399-402.
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License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation.
He, S., Jia, W. & Wu, Q. 2008, 'An Approach of Canny Edge Detection with Virtual Hexagonal Image Structure', International Conference on Control, Automation, Robotics and Vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Hanoi, Vietnam, pp. 879-882.
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Edge detection plays an important role in the areas of image processing, multimedia and computer vision. Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that, in the human vision system, the edge points always appear where the gradient magnitude assumes a maximum. Hexagonal structure is an image structure alternative to traditional square image structure. The geometrical arrangement of pixels on a hexagonal structure can be described as a collection of hexagonal pixels. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, an approach that uses bilinear interpolation and tri-linear interpolation is applied for conversion between square and hexagonal structures. Based on this approach, an edge detection method is proposed. This method performs Gaussian filtering to suppress image noise and computes gradients on the hexagonal structure. The pixel edge strengths on the square structure are then estimated before Canny's edge detector is applied to determine the final edge map. The experimental results show that the proposed method improves the edge detection accuracy and efficiency.
Chen, Y., Wu, Q., He, S., Du, C. & Yang, J. 2008, 'Extracting Key Postures Using Radon Transform', Image and Vision Computing New Zealand (IVCNZ 2008), Image and Vision Computing Conference, IEEE, Christchurch, New Zealand, pp. 1-5.
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Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient.
Chen, Y., Wu, Q., He, S., Jia, W. & Hintz, T.B. 2008, 'A Modified Mahalanobis Distance for Human Detection in Out-door Environments', First IEEE International Conference on Ubi-media Computing (U-Media 2008), U-Media, IEEE, Lanzhou, China, pp. 243-248.
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This paper proposes a novel method for human detection from static images based on pixel structure of input images. Each image is divided into four parts, and a weight is assigned to each part of the image. In training stage, all sample images including human images and non-human images are used to construct a Mahalanobis distance map through statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Mahalanobis distance map. This projection matrix will be used to transform multi-dimensional feature vectors into one dimensional feature domain according to a pre-calculated threshold to distinguish human figures from non-human figures. In comparison with the method without introducing weights, the proposed method performs much better. Encouraging experimental results have been obtained based on MIT dataset and our own dataset.
He, S., Zheng, L., Wu, Q., Jia, W., Samali, B. & Palaniswami, M.S. 2008, 'A hierarchically combined classifier for license plate recognition', IEEE 8th International Conference on Computer and Information Technology (CIT2008), IEEE International Conference on Computer and Information Technology, IEEE Computer Society, Sydney, pp. 372-377.
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High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an inductive learning based method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for further fast training and testing processes for SVM-based classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVM-based classification in terms of error rates and processing speeds.
Chen, Y., Wu, Q. & He, S. 2008, 'Motion Based Pedestrian Recognition', 2008 International Congress on Image and Signal Processing (CISP2008), International Congress on Image and Signal Processing, IEEE Computer Society, Sanya, China, pp. 376-380.
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This paper proposed a method for discriminating pedestrians from rigid objects in a video. The method is a motion-based recognition of moving objects. This method is motivated by the assumptions that human beings are non-rigid and their movements are periodic. Moving objects and their skeletons are extracted. The motion cue is determined by the angle formed by the centroid point and the two bottom end points at objects skeleton. The histogram of the cue over a time period is used to determine if the object is pedestrian or not. This cue does not require any pre-built models. Neither does it need Fourier Transform to obtain the cycle of the objects. The proposed method is computation inexpensive, and it can be used for realtime video surveillance.
Jiang, Y., Chen, Y., Zeng, Z. & He, S. 2008, 'A bank customer credit evaluation based on the decision tree and the simulated annealing algorithm', 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, IEEE International Conference on Computer and Information Technology, IEEE, Sydney, NSW, pp. 203-206.
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Chen, Y., Wu, Q. & He, S. 2008, 'Human Action Recognition by Radon Transform', IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08., IEEE International Conference on Data Mining, IEEE, Pisa, Italy, pp. 862-868.
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A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear discriminant analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors. Different classification methods are used to classify each sequence. Experiments are carried out based on a publically available human behaviour database and the results are exciting.
He, X. & Wu, Q. 2008, 'Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008: Message from chairs', Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008.
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Chen, Y., Wu, Q., He, S., Jia, W. & Hintz, T.B. 2007, 'Pixel Structure Based on Hausdorff Distance for Human Detection in Outdoor Environments', Digital Image Computing Techniques and Applications, Digital Image Computing Techniques and Applications, IEEE Computer Society, Glenelg, Australia, pp. 67-72.
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This paper proposes a novel method for human detection from static images based on pixel structure of input images. In training stage, all sample images consisting of human images and non-human images are used to construct a Hausdorff distance map based on statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Hausdorff distance map. This projection matrix will be used to transform multidimensional feature vectors (distance maps of testing images) into a feature in a one-dimensional domain. The decision will be made on the simple one dimensional feature domain according to a precalculated threshold to distinguish human figures from non-human figures. In comparison with the common method based on Mahalanobis distance maps, the proposed method based on Hausdorff distance maps performs much better. Encouraging experimental results have been obtained using 800 human images and 800 non-human images.
He, S., Jia, W., Li, J., Wu, Q. & Hintz, T.B. 2007, 'An Approach to Edge Detection on a Virtual Hexagonal Structure', Digital Image Computing Techniques and Applications, Digital Image Computing Techniques and Applications, IEEE Computer Society, Glenelg, Australia, pp. 340-345.
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Hexagonal structure is another image structure alternative to traditional square image structure for image processing and computer vision. The geometrical arrangement of pixels on a hexagonal structure can be described as a collection of hexagonal pixels. Because all the existing hardware for capturing image and for displaying image are produced based on square structure, it becomes important to find a proper software approach to mimic hexagonal structure so that images represented on the traditional square structure can be smoothly converted from or to the images on hexagonal structure. For accurate image processing, it is critical to best maintain the image resolution during the image conversion. In this paper, a bilinear interpolation algorithm that is used to convert an image from square structure to hexagonal structure is presented. Based on this, an edge detection method is proposed. Our experimental results show that the bilinear interpolation improves the edge detection accuracy.
He, S., Zhang, H., Jia, W., Wu, Q. & Hintz, T.B. 2007, 'Combining Global and Local Features for Detection of License Plates in Video', Proceedings of Image and Vision Computing New Zealand 2007, Image and Vision Computing Conference, Image and Vision Computing New Zealand, Hamilton, New Zealand, pp. 288-293.
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Jia, W., He, S., Zhang, H. & Wu, Q. 2007, 'Combining Edge and Colour Information for Number Plate Detection', Proceedings of Image and Vision Computing New Zealand 2007, Image and Vision Computing Conference, Image and Vision Computing New Zealand, Hamilton, New Zealand, pp. 227-232.
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This paper presents a method for vehicle number plate detection which combines edge and colour features of number plates. We concentrate on two key issues of this application: speed and robustness. Our focus is put on detecting parts of a number plate, instead of the number plate itself as a whole. To achieve the target of real-time detection, two simple features based on a redeÂned vertical edge map are constructed. To address the illumination-sensitive problem of using colour information, a Gaussian weighted histogram intersection (GWHI) method is proposed. The above new ideas compose the major part of the algorithm. Our experimental results demonstrate a promising preliminary result on detecting yellow number plates in terms of detection speed and robustness, which shows the feasibility of the proposed method.
Wu, Q., Wang, L., Geng, X., Li, M. & He, S. 2020, 'Dynamic Biometrics Fusion at Feature Level for Video-Based Human Recognition', Proceedings of Image and Vision Computing New Zealand 2007, Image and Vision Computing Conference, Image and Vision Computing New Zealand, Hamilton, New Zealand, pp. 152-157.
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He, S., Li, J., Jia, W., Wu, Q. & Hintz, T.B. 2007, 'Local Binary Patterns on Hexagonal Image Structure', Proceedings of 7th IEEE International Conference on Computer and Information Technology, IEEE International Conference on Computer and Information Technology, IEEE, Aizu-Wakamatsu City, Fukushima, Japan, pp. 639-644.
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Local binary pattern (LBP) was designed and widely used for efficient texture classification. It has been used for face recognition and has potential applications in many other research areas such as human detection. LBP provides a simple and effective way to represent patterns. Uniform LBPs play an important role for LBP-based pattern /object recognition as they include majority of LBPs. In this paper, we present LBP codes on hexagonal image structure. We show that LBPs defined on hexagonal structure have higher percentages of uniform LBPs that will lead to a more efficient and accurate recognition scheme for image classification.
He, S., Li, J., Chen, Y., Wu, Q. & Jia, W. 2007, 'Local Binary Patterns for Human Detection on Hexagonal Structure', Proceedings of the 2007 IEEE International Symposium on Multimedia (ISM-07), IEEE International Symposium on Multimedia, IEEE Computer Society, Taichung, Taiwan, pp. 65-71.
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Local Binary Pattern (LBP) was designed and has been widely used for efficient texture classification. LBP provides a simple and effective way to represent texture patterns. Uniform LBPs play an important role for LBP-based pattern/object recognition as they include majority of LBPs. On the other hand, Human detection based on Mahalanobis Distance Map (MDM) recognizes appearance of human based on geometrical structure. Each MDM shows a clear texture pattern that can be classified using LBPs. In this paper, we compute LBPs of MDMs on a hexagonal structure. The circular pixel arrangement in hexagonal structure results in higher accuracy for LBP representation than on square structure. Chi-square as a measure is used for human detection based on uniform LBPs obtained. We show that our method using LBPs built on MDMs has a higher human detection rate and a lower false positive rate compared to the method merely based on MDMs. We will also show using experimental results that LBPs on hexagonal structure lead to more robust human classification.
Jia, W., Tien, D., He, S., Hope, B.A. & Wu, Q. 2007, 'Applying Local Cooccurring Patterns for Object Detection from Aerial Images', International Conference on Visual Information Systems - Lecture Notes in Computer Science, International Conference on Visual Information Systems, Springer, Shanghai, China, pp. 478-489.
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Developing a spatial searching tool to enhance the search capabilities of large spatial repositories for Geographical Information System (GIS) update has attracted more and more attention. Typically, objects to be detected are represented by many local features or local parts. Testing images are processed by extracting local features which are then matched with the objectâs model image. Most existing work that uses local features assumes that each of the local features is independent to each other. However, in many cases, this is not true. In this paper, a method of applying the local cooccurring patterns to disclose the cooccurring relationships between local features for object detection is presented. Features including colour features and edge-based shape features of the interested object are collected. To reveal the cooccurring patterns among multiple local features, a colour cooccurrence histogram is constructed and used to search objects of interest from target images. The method is demonstrated in detecting swimming pools from aerial images. Our experimental results show the feasibility of using this method for effectively reducing the labour work in finding man-made objects of interest from aerial images.
He, S., Jia, W., Wu, Q. & Hintz, T.B. 2007, 'Parallel Edge Detection on a Virtual Hexagonal Structure', International Conference on Grid and Pervasive Computing - Lecture Notes in Computer Science, International Conference on Grid and Pervasive Computing, Springer, Paris, France, pp. 751-756.
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This paper presents an edge detection method based on bilateral filtering taking into account both spatial closeness and intensity similarity of pixels in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on sampled images represented on a newly developed virtual hexagonal structure. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained. We also present a parallel implementation for edge detection on the virtual hexagonal structure that significantly increases the computation speed.
He, S., Hintz, T.B., Li, J., Zhang, H., Wu, Q. & Jia, W. 2007, 'Local Binary Pattern on Hexagonal Structure for Face Matching', Proceedings of the 2007 International Conference on Image Processing, Computer Vision and Pattern Recognition, International Conference on Image Processing, Computer Vision and Pattern Recognition, CSREA Press, Las Vegas, USA, pp. 455-460.
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Dong, W., He, S. & Hintz, T.B. 2007, 'A Robust Hough Transform Based Method for Direction Detection and Its Application', IPCV2007, International Conference on Image Processing, Computer Vision and Pattern Recognition, CSREA Press, Las Vegas, pp. 439-443.
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He, S., Li, J. & Hintz, T.B. 2007, 'Comparison of Image Conversions between Square Structure and Hexagonal Structure', Advanced Concepts for Intelligent Vision Systems - Lecture Notes in Computer Science, Advanced Concepts for Intelligent Vision Systems, Springer Berlin / Heidelberg, Delft, The Netherlands, pp. 262-273.
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Song, J., He, S. & Lin, Z. 2007, 'Global Optimization under Nonlinear Constraints Based on Apollonius Fill', Proceedings of the 3rd International Conference on Natural Computation, International Conference on Natural Computation, IEEE Computer Society, Hainan, China, pp. 39-43.
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Making use of Apollonius Fill, an algorithm is presented, which is for finding solutions of global optimization problems nonlinearly constrained by a circular region in the plane. Using this algorithm, global optimum can be computed fast and precisely. We request no more than first order derivatives of objective functions for the optimization algorithm. If we do not care about the processing time taken, for any given objective function, the global optimum can be obtained as precisely as requested. The proof of convergence of this algorithm is also given in this paper. We use a few numerical examples to show that this algorithm is effective, reliable, and hence is valuable in practice.
He, S., Wu, Q., Zhang, H. & Hintz, T.B. 2007, 'A Trend for Face Recognition', Proceedings of the 4th International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 254-257.
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Song, J., He, S. & Qian, F. 2007, 'Fractal Algorithm for Finding Global Optimal Solution', Proceedings of the 4th International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 258-261.
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He, S., Li, J. & Hur, N. 2007, 'Bilinear Interpolation on a Virtual Hexagonal Structure', Proceedings of the 4th International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 469-472.
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Zheng, L., He, S., Wu, Q. & Hintz, T.B. 2020, 'Number Plate Recognition without Segmentation', Proceedings of Image and Vision Computing New Zealand 2007, Image and Vision Computing Conference, Image and Vision Computing New Zealand, Hamilton, New Zealand, pp. 164-168.
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Chen, Y., Wu, Q. & He, S. 2007, 'Study on Human Behavior Retrieval', IPCV2007, International Conference on Image Processing, Computer Vision and Pattern Recognition, CSREA Press, Las Vegas, pp. 448-454.
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Chen, Y., Wu, Q., He, X., Jia, W. & Hintz, T. 2007, 'Pixel structure based on Hausdorff distance for human detection in outdoor environments', Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007, pp. 67-72.
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This paper proposes a novel method for human detection from static images based on pixel structure of input images. In training stage, all sample images consisting of human images and non-human images are used to construct a Hausdorff distance map based on statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Hausdorff distance map. This projection matrix will be used to transform multi-dimensional feature vectors (distance maps of testing images) into a feature in a one-dimensional domain. The decision will be made on the simple one-dimensional feature domain according to a precalculated threshold to distinguish human figures from non-human figures. In comparison with the common method based on Mahalanobis distance maps, the proposed method based on Hausdorff distance maps performs much better. Encouraging experimental results have been obtained using 800 human images and 800 non-human images. © 2007 IEEE.
He, X., Li, J., Chen, Y., Wu, Q. & Jia, W. 2007, 'Local binary patterns for human detection on hexagonal structure', Proceedings - 9th IEEE International Symposium on Multimedia, ISM 2007, pp. 65-71.
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Local Binary Pattern (LBP) was designed and has been widely used for efficient texture classification. LBP provides a simple and effective way to represent texture patterns. Uniform LBPs play an important role for LBP-based pattern/object recognition as they include majority of LBPs. On the other hand, Human detection based on Mahalanobis Distance Map (MDM) recognizes appearance of human based on geometrical structure. Each MDM shows a clear texture pattern that can be classified using LBPs. In this paper, we compute LBPs of MDMs on a hexagonal structure. The circular pixel arrangement in hexagonal structure results in higher accuracy for LBP representation than on square structure. Chi-square as a measure is used for human detection based on uniform LBPs obtained. We show that our method using LBPs built on MDMs has a higher human detection rate and a lower false positive rate compared to the method merely based on MDMs. We will also show using experimental results that LBPs on hexagonal structure lead to more robust human classification. © 2007 IEEE.
Wang, H., He, S., Wu, Q. & Hintz, T.B. 2006, 'A new approach for Fractal Image Compression on a virtual hexagonal structure', 18Th International Conference On Pattern Recognition, Vol 3, Proceedings, International Conference on Pattern Recognition, IEEE Computer Soc, Hong Kong, China, pp. 909-912.
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In this paper, we propose a Fractal Image Compression method on a virtual hexagonal image structure by adopting Fisher's basic method on the traditional square image structure. The modification on the definition of range block and domain block is impleme
Wang, H., Wu, Q., He, S. & Hintz, T.B. 2006, 'A novel interactive progressive decoding method for fractal image compression', Icicic 2006: First International Conference On Innovative Computing, Information And Control, Vol 3, Proceedings, International Conference on Innovative Computing, Information and Control, IEEE Computer Soc, Beijing, PEOPLES R CHINA, pp. 613-616.
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Fractal image compression is an efficient and effective technique in image coding. This paper presents a novel interactive progressive fractal decoding method, with which the compressed file can be transmitted incrementally and reconstructed progressivel
Jia, W., Zhang, H., He, S. & Wu, Q. 2006, 'Symmetric color ratio in spiral architecture', Computer Vision - ACCV 2006, Pt ii, Lecture Notes in Computer Science, Asian Conference on Computer Vision, Springer-Verlag Berlin, Hyderabad, India, pp. 204-213.
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Color ratio gradient (CRG) is a robust method used for color image retrieval and object recognition. It has been proven to be illumination-independent and geometry-insensitive when tested on scenery images. However, the color ratio gradient produces unsa
Wu, Q., Zhang, H., Jia, W., He, S., Yang, J. & Hintz, T.B. 2006, 'Car plate detection using cascaded tree-style learner based on hybrid object features', Proceedings of international conference on video and signal based surveillance 2006, Advanced Video and Signal Based Surveillance, IEEE, Sydney, Australia, pp. 1-6.
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Wang, H., He, S., Wu, Q. & Hintz, T.B. 2006, 'Improvement of fractual image coding base on the different image', WISTSP '06 proceedings, Workshop in Information Security Theory and Practices, DSP for communication systems, Hobert, Australia, pp. 1-5.
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Wang, H., Wu, Q., He, S. & Hintz, T.B. 2006, 'Prelimenary research on fractual video compression on spiral architecture', IPCU 2006 Proceeding, International Conference on Image Processing, Computer Vision and Pattern Recognition, CSREA Press, Las Vegas, USA, pp. 557-562.
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Jia, W., Zhang, H., He, S. & Wu, Q. 2006, 'Image matching using color edge coocurrence histogram', Proceedings of the 2006 IEEE international conference on systems, man and cybernetics, IEEE Conference on Systems, Man and Cybernetics, IEEE, Taipei, Taiwan, pp. 2413-2419.
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In this paper, a novel colour edge cooccurrence histogram (CECH) method is proposed to match images by measuring similarities between their CECH histograms. Unlike the previous colour edge cooccurrence histogram proposed by Crandall and Luo (2004 ) we only investigate those pixels which are located at the two sides of edge points in their gradient direction lines and at a distance away from the edge points. When measuring similarities between two CECH histograms, a newly proposed Gaussian weighted histogram intersection (GWHI) method is extended for this purpose. Both identical colour pairs and similar colour pairs are taken into account in our algorithm, and the weights are decided by the larger distance between two colour pairs involved in matching. The proposed algorithm is tested for matching vehicle number plate images captured under various illumination conditions. Experimental results demonstrate that the proposed algorithm can be used to compare images in real-time, and is robust to illumination variations and insensitive to the model images selected.
Jia, W., He, S. & Tien, D. 2006, 'Automatically detecting road sign text from natural scene video', Proceedings of IEEE region 10 conference 2006, IEEE Tencon (IEEE Region 10 Conference), IEEE, Hong Kiong, pp. 1-4.
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Wang, H., Wu, Q., He, S. & Hintz, T.B. 2006, 'A new approach for SA-based fractal image compression', ICIP 2006 Proceedings, IEEE International Conference on Image Processing, IEEE, Atlanta, USA, pp. 3101-3104.
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Spiral Architecture based fractal image compression is proposed in this paper. Perceptually, a new definition of range block and domain block is presented on such enhanced image structure. Compared with the common square image architecture, spiral architecture provides higher fidelity to fractal image compression, which is demonstrated by the experimental results.
He, S., Hintz, T.B., Wu, Q., Wang, H. & Jia, W. 2006, 'A new simulation of spiral architecture', IPCU 06 procedings, International Conference on Image Processing, Computer Vision and Pattern Recognition, CSREA Press, Las Vegas, USA, pp. 570-575.
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He, S., Wang, H., Hur, N., Jia, W., Wu, Q., Kim, J.C. & Hintz, T.B. 2006, 'Uniformly partitioning images on a virtual hexagonal structure', 2006 8th International conference on control automation robotics and vision (ICARCV 2006), International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 891-896.
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Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexagonal structure can be described in terms of a hexagonal grid. Uniformly separating image into seven similar copies with a smaller scale has commonly been used for parallel and accurate image processing on hexagonal structure. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. Furthermore, the current techniques used for uniform separation of images on hexagonal structure do not coincide with the rectangular shape of images. This has been an obstacle in the use of hexagonal structure for image processing. In this paper, we briefly review a newly developed virtual hexagonal structure that is scalable. Based on this virtual structure, algorithms for uniform image separation are presented. The virtual hexagonal structure retains image resolution during the process of image separation, and does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure while the image shape is kept in rectangle
Jia, W., Zhang, H., He, S. & Wu, Q. 2006, 'Refined gaussian weighted histogram intersection and its application in number plate categorization', Proceedings, computer graphics, imaging and visualisation, International Conference Computer Graphics, Imaging and Visualization, IEEE Computer Society, Sydney Australia, pp. 249-254.
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This paper proposes a refined Gaussian weighted histogram intersection for content-based image matching and applies the method for number plate categorization. Number plate images are classified into two groups based on their colour similarities with the model image of each group. The similarities of images are measured by the matching rates between their colour histograms. Histogram intersection (HI) is used to calculate the matching rates of histograms. Since the conventional histogram intersection algorithm is strictly based on the matching between bins of identical colours, the final matching rate could easily be affected by colour variation caused by various environment changes. In our recent paper [9], a Gaussian weighted histogram intersection (GWHI) algorithm has been proposed to facilitate the histogram matching via taking into account matching of both identical colours and similar colours. The weight is determined by the distance between two colours. When applied to number plate categorization, the GWHI algorithm demonstrates to be more robust to colour variations and produces a classification with much lower intra-class distance and much higher interclass distance than previous HI algorithms. However, the processing speed of this GWHI method is still not satisfying. In this paper, the GWHI method is further refined, where a colour quantization method is utilized to reduce the number of colours without introducing apparent perceptual colour distortion. New experimental results demonstrate that using the refined GWHI method, image categorization can be done more efficiently.
Zheng, L., He, S., Wu, Q. & Hintz, T.B. 2006, 'Learning based number recognition on spiral architecture', Proceedings of 2006 9th international conference on control, automation, robotics and vision, International Conference on Control, Automation, Robotics and Vision, IEEE, Grand Hyatt, Singapore, pp. 897-901.
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In this paper, a number recognition algorithm is proposed on spiral architecture, a hexagonal image structure. This algorithm employs RULES-3 inductive learning method to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Edge maps of the samples are then detected based on spiral architecture. A set of rules are extracted using these samples by RULES-3. The rules describe the frequencies of 9 different edge masks appearing in the samples. Each mask is a cluster of 7 hexagonal pixels. In order to recognize a number plate, all numbers are tested one by one using the extracted rules. The number recognition is achieved by counting the frequencies of the 9 masks. In this paper, a comparison between results based on rectangular structure and the results based on spiral architecture is given. From the experimental results, we can make the conclusion that Spiral Architecture is better than rectangular structure for inductive learning-based number recognition
Zhang, H., Jia, W., He, S. & Wu, Q. 2006, 'A fast algorithm for license plate detection in various conditions', Proceedings of the 2006 IEEE International Conference on System, Man and Cybernetics, IEEE Conference on Systems, Man and Cybernetics, IEEE, Taibei, China, pp. 2420-2425.
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This paper proposes a fast algorithm detecting license plates in various conditions. There are three main contributions in this paper. The first contribution is that we define a new vertical edge map, with which the license plate detection algorithm is extremely fast. The second contribution is that we construct a cascade classifier which is composed of two kinds of classifiers. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-features, which make it possible to detect license plate in various conditions. Our algorithm is robust to the variance of the illumination, view angle, the position, size and color of the license plates when working in complex environment. The third contribution is that we experimentally analyze the relations of the scaling factor with detection rate and processing time. On the basis of the analysis, we select the optimal scaling factor in our algorithm. In the experiments, both high detection rate (with low false positive rate) and high speed are achieved when the algorithm is used to detect license plates in various complex conditions.
He, S., Jia, W., Wu, Q., Hur, N., Hintz, T.B., Wang, H. & Kim, J.C. 2006, 'Basic transformation on virtual hexagonal structure', Proceedings. 2006 international conference on computer graphics, imaging and visualisation, International Conference Computer Graphics, Imaging and Visualization, IEEE Computer society, Sydney, Australia, pp. 243-248.
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Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexagonal structure can be described in terms of a hexagonal grid. Hexagonal structure provides an easy way for image translation and rotation transformations. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. In this paper, we introduce a new virtual hexagonal structure. Based on this virtual structure, a more flexible and powerful image translation and rotation are performed. The virtual hexagonal structure retains image resolution during the process of image transformations, and does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure.
He, S., Jia, W., Hur, N., Wu, Q. & Kim, J.C. 2006, 'Image translation and rotation on hexagonal structure', Sixth IEEE International conference on computer and information technology, IEEE International Conference on Computer and Information Technology, IEEE CS, Seoul, Korea, pp. 1-6.
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Image translation and rotation are becoming essential operations in many application areas such as image processing, computer graphics and pattern recognition. Conventional translation moves image from pixels to pixels and conventional rotation usually comprises of computation-intensive CORDIC operations. Traditionally, images are represented on a square pixel structure. In this paper, we perform reversible and fast image translation and rotation based on a hexagonal structure. An image represented on the hexagonal structure is a collection of hexagonal pixels of equal size. The hexagonal structure provides a more flexible and efficient way to perform image translation and rotation without losing image information. As there is not yet any available hardware for capturing image and for displaying image on a hexagonal structure, we apply a newly developed virtual hexagonal structure. The virtual hexagonal structure retains image resolution during the process of image transformations, and almost does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure.
Gao, F., He, S., Li, Y. & Wang, J. 2006, 'MAC performance analysis on two stage HFC access networks', IEEE international conference on computer and information technology (CIT 2006), IEEE International Conference on Computer and Information Technology, IEEE CS, Seoul, Korea, pp. 96-99.
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Two-stage access network structure is widely used in fixed and mobile access networks such as HFC (Hybrid Fibre/Coaxial) and BSS (Base Station System). In this paper, we introduce a new two-stage access scheme that employs random access and centralized polling methods corresponding to the two stages respectively. The new schemeÂs MAC (Medium Access Control) protocol procedure is presented based on DOCSIS standard. The performance of the mechanism in terms of access delay and throughput is evaluated and analyzed using computer simulating experiments. The results show the new access mechanism is efficient.
He, S., Zhang, H., Hur, N., Kim, J., Wu, Q. & Kim, T. 2006, 'Estimation of internal paramenters and external paramenters for camera calibration using ID pattern', Proceedings of the international conference on video and signal based surveillance 2006 (AVSS 2006), Advanced Video and Signal Based Surveillance, IEEE Computer Society, Sydney, Australia, pp. 93-98.
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He, S., Zhang, H., Hur, N., Kim, J., Kim, T. & Wu, Q. 2006, 'Complete camera calibration using line-shape objects', Tencon 2006 Hong Kong IEE region 10 conference, IEEE Tencon (IEEE Region 10 Conference), IEEE, Honk Kong, China, pp. 1-4.
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Zheng, L. & He, S. 2006, 'Number plater recognition based on SVMs', Proceeding of the IEEE international conference on advanced video and signal based surveillance 2006, Advanced Video and Signal Based Surveillance, IEEE, Sydney, Australia, pp. 1-5.
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Jia, W., Zhang, H., He, S. & Wu, Q. 2006, 'A comparison on histogram based image matching methods', Proceedings of the IEEE international conference on video and signal based surveillance, Advanced Video and Signal Based Surveillance, IEEE Computer Society, Sydney, Australia, pp. 1-6.
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He, S., Wang, H., Wu, Q., Hintz, T.B. & Hur, N. 2006, 'Fractal image compression on spiral architecture', Proceeding 2006 international conference on computer graphics imaging and visualisation, International Conference Computer Graphics, Imaging and Visualization, IEEE computer society, Sydney, Australia, pp. 76-83.
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Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS?s through the construction of a Complete Metric Space on SA. The selection of range and domain blocks for fractal image compression is highly related to the uniform image separation specific to SA. In this paper, we will review the current research work on fractal image compression based on SA. We will compare the results obtained on SA and the traditional square structure in terms of compression ratio and PSNR.
Zhang, H., Jia, W., He, S. & Wu, Q. 2006, 'Learning based license plate detection using global and local features', 18th International Conference on Pattern Recognition, International Conference on Pattern Recognition, IEEE Computer Society, Hong Kong, pp. 1102-1105.
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This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments
Zhang, H., Jia, W., He, S. & Wu, Q. 2006, 'Real-time license plate detection under various conditions', Third International Conference, International Conference on Ubiquitous and Intelligence Computing 2006 - Lecture Notes in Computer Sciences, International Conference on Ubiquitous and Intelligence Computing, Springer, Wuhan, China, pp. 192-199.
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This paper proposes an algorithm for real-time license plate detection. In this algorithm, the relatively easy car plate features are adopted including the simple statistical feature and Harr-like feature. The simplicity of the object features used is very helpful to real-time processing. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
Jia, W., Zhang, H., He, S. & Wu, Q. 2006, 'Gaussian weighted histogram intersection for license plate classification', 18Th International Conference On Pattern Recognition, Vol 3, Proceedings, International Conference on Pattern Recognition, IEEE Computer Soc, Hong Kong, PEOPLES R CHINA, pp. 574-577.
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The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between b
He, S., Jia, W., Hur, N., Wu, Q., Kim, J. & Hintz, T.B. 2006, 'Bi-lateral edge detection on a virtual hexagonal structure', International Symposium on Visual Computing 2006 - Lecture notes in computing science, International Symposium on Visual Computing, Springer, United States, pp. 176-185.
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Liu, J., Li, S., He, S. & Wu, Q. 2005, 'A study of fractal based watermarking for images', DCABES And ICPACE Joint Conference On Distributed Algorithms For Science And Engineering, Joint Meeting of the International Symposium on Distributed Computing and Applications to Business, Engineering and Science/International Conference on Parallel Algorithms and Computing Evironments, CMS Press, Brighton, England, pp. 127-130.
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In this paper, we will provide a study on Fractal based watermarking techniques available today. Fractal is a technique that makes use of the similarity of the natural phenomena of irregular shapes. Only in recent years it has been used in image coding a
Zheng, L. & He, S. 2005, 'A study on classification techniques for pattern recognition', DCABES And ICPACE Joint Conference On Distributed Algorithms For Science And Engineering, Joint Meeting of the International Symposium on Distributed Computing and Applications to Business, Engineering and Science/International Conference on Parallel Algorithms and Computing Evironments, CMS Press, Brighton, England, pp. 161-164.
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In this paper, the general process of pattern recognition system is extracted. This process includes preprocessing, feature selection and extraction, classification, and optimization. Following this processing chain, each stage is discussed in detail and
Wu, Q., He, S. & Hintz, T.B. 2005, 'Bi-lateral filtering based edge detection on hexagonal architecture', 2005 IEEE International Conference On Acoustics, Speech, And Signal Processing, Vols 1-5 - Speech Processing, IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Philadelphia, PA, USA, pp. 11713-11716.
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Edge detection plays an important role in image processing area but is still an open problem. This paper presents a novel edge detection method based on bi-lateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on hexagonally sampled images. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained on hexagonal architecture than common edge detection on square architecture. Experimental results using our proposed method in this paper exhibit encouraging performance.
Zhang, H., Jia, W., He, S. & Wu, Q. 2005, 'Modified Colour Ratio Gradients', 2005 IEEE Seventh workshop on Multimedia Signal Processing, International Workshop on Multimedia Signal Processing, IEEE, Shanghai, China, pp. 317-320.
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Color ratio gradient is an efficient method used for color image retrieval and object recognition, which is shown to be illumination-independent and geometry-insensitive when tested on scenery images. However, color ratio gradient produces unsatisfied matching result while dealing with relatively uniform objects without rich color texture. In addition, performance of color ratio gradient degenerates while processing unsaturated color image objects. In this paper, a scheme with modified color ratio gradient is presented, which addresses the two problems above. Experimental results using the proposed method in this paper exhibit more robust performance
He, S., Hintz, T.B., Wu, Q. & Zheng, L. 2005, 'Number Recognition Using Inductive learning on Spiral Architecture', Proceedings of the 2005 international conference in computer vision, International conference in computer vision, CSREA Press, Las Vegas, USA, pp. 58-62.
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Zheng, L., He, S. & Li, Y. 2005, 'A comparison of methods for character recognition of car number plates', Proceedings of the 2005 International conference on computer vision, International conference on computer vision, CSREA Press, Las Vegas, Nevada, USA, pp. 33-39.
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He, S., Wang, H., Wu, Q. & Hintz, T.B. 2005, 'Contractive IFS for fractal image compression on spiral architecture', Proceedings of 2005 Asia-Pacific Workshop on Visual Information Processing, Asia-Pacific Workshop on Visual Information Processing, IEEE, Hong Kong, China, pp. 171-176.
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Liu, J. & He, S. 2005, 'A review study on digital watermarking', Proceedings of IBA ICIT 2005, International Conference on Information and Communications Technology, IEEE, Karachi, Pakistan, pp. 337-341.
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There has been a rapid development in the growth of multimedia and communications in the past few years. With the widespread use of the networks, intellectual properties can be obtained, reproduced or redistributed easily. This creates a high demand for content protection techniques. Watermarking, one of the most efficient ways to protect the digital properties has won more and more concern in recent years. This paper reviews several aspects about digital watermarking. Among them, we mainly focus on three processes of digital watermarking. Also, we will examine the properties of watermarking and several applications of watermarking. At last, this paper will discuss the differences between watermarking, steganography and cryptography.
Liu, D. & He, S. 2005, 'Achieving fast fractal image compression using spiral architecture', Proceedings of the 2005 international conference on parallel and distributed processing techniques and applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 141-146.
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He, S. & Jia, W. 2005, 'Hexagonal structure for intelligent vision', Proceedings of IBS ICICT 2005 1st International conference on information and communication technologies, International Conference on Information and Communications Technology, IEEE, Karachi, Pakistan, pp. 52-64.
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Using hexagonal grids to represent digital images have been studied for more than 40 years. Increased processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications and brought new interests on this topic. The hexagonal structure is considered to be preferable to the rectangular structure due to its higher sampling efficiency, consistent connectivity and higher angular resolution and is even proved to be superior to square structure in many applications. Since there is no mature hardware for hexagonal-based image capture and display, square to hexagonal image conversion has to be done before hexagonal-based image processing. Although hexagonal image representation and storage has not yet come to a standard, experiments based on existing hexagonal coordinate systems have never ceased. In this paper, we firstly introduced general reasons that hexagonally sampled images are chosen for research. Then, typical hexagonal coordinates and addressing schemes, as well as hexagonal based image processing and applications, are fully reviewed.
Liu, D. & He, S. 2005, 'Models of the sea mixed waves in navigating radar simulator', Proceedings of Third International Conference On Information Technology And Applications, Vol 1, International Conference on Information Technology and Applications, IEEE, Sydney, Australia, pp. 634-637.
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The sea mixed wave models of Radar simulators in existence only use the random number produced by computer to simulate simply ..don?t use the statistic discipline and mathematical model, the effect of simulation is not good, the fidelity of radar echo image is affected. The echo intensity of the sea mixed wave accords with Weibull distribution is discovered through researching on the statistic rule of the sea mixes wave. The inverse function of Weibull?s probability distribution function has been used to random numbers with Weibull distribution. The shape parameter and intensity parameter of Weibull distribution is given combined with the experimental accumulation and the distributing circumstance of real radar's sea mixed wave under different range and sea situation.
Jia, W., Zhang, H., He, S. & Piccardi, M. 2005, 'Mean shift for accurate license plate localisation', ITSC '05 - 8th International Conference on Intelligent Transportation Systems, International Conference on Intelligent Transportation Systems, IEEE, Vienna, Austria, pp. 566-571.
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Wang, H., Wang, M., Hintz, T.B., He, S. & Wu, Q. 2005, 'Fractal image compression on a pseudo spiral architecture', Proceedings of the Twenty Eighth Australasian Computer Science Conference (ACSC2005), Australasian Computer Science Conference, ACM, Newcastle, Aust, pp. 201-207.
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Fractal image compression is a relatively recent image compression method which exploits similarities in different parts of the image. The basic idea is to represent an image by fractals and each of which is the fixed point of an Iterated Function System (IFS). Therefore, an input image can be represented by a series of IFS codes rather than pixels. In this way, an impressive compression ratio 10000:1 can be achieved. The application of fractal image compression presented in this paper is based on a novel image structure, Spiral Architecture, which has hexagonal instead of square pixels as the basic element. In the paper evidence would suggest that introducing Spiral Architecture into fractal image compression will improve the compression performance in compression ratio with little suffering in image quality. There are also much research could be done in this area to further improve the results.
He, S., Jia, W., Lin, Q. & Hintz, T.B. 2005, 'Detection of Heart Movement Manner Based on Hexagonal Image Structure', Proceedings of the First International Conference on Medical Imaging and Telemedicine, Middlesex University Press, Wuyi Mountain, P. R. China, pp. 28-31.
Zheng, L. & He, S. 2005, 'Classification Techniques in Pattern Recognition', 13th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005 Poster Proceedings, International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, Union Agency - Science Press, Plzen, Czech Republic, pp. 77-78.
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In this paper, we review some pattern recognition schemes published in recent years. After giving the general processing steps of pattern recognition, we discuss several methods used for steps of pattern recognition such as Principal Component Analysis (PCA) in feature extraction, Support Vector Machines (SVM) in classification, and so forth. Different kinds of merits are presented and their applications on pattern precognition are given. The objective of this paper is to summarize and compare some of the methods for pattern recognition, and future research issues which need to be resolved and investigated further are given along with the new trends and ideas.
Jia, W., Zhang, H. & He, S. 2005, 'Mean shift for accurate number plate detection', Proceedings of Third International Conference On Information Technology And Applications, Vol 1, International Conference on Information Technology and Applications, IEEE, Sydney, Australia, pp. 732-737.
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This paper presents a robust method for number plate detection, where mean shift segmentation is used to segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region contains a number pla
Abachi, H., Alameldin, T., Alsultanny, Y.A., Arabnia, H.R., Demetriou, G.A., Fiaidhi, J., Girija, P.N., Grawanis, G.A., He, X., Hintz, T., Joshua, R., Karam, J.R., Kato, H., Li, K.C., Li, P., Li, X., Li, X., Luo, Y., Mayer, J., Mohammed, S., Mun, Y., Ponalagusamy, R., Ro, Y.M., Schaefer, G., Sim, K.S., Sirakov, N.M., Shrikumar, H., Vasikarla, S., Watson, L.T., Wu, Q., You, J., Zadeh, J., Zhang, C. & Zhu, Y. 2005, 'Proceedings of the 2005 international conference on imaging science, systems, and technology: Computer graphics, CISST'05: foreword', Proceedings of the 2005 International Conference on Imaging Science, Systems, and Technology: Computer Graphics, CISST'05.
Hintz, T., Piccardi, M. & He, X. 2005, 'Message from the Chairs', Proceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005, p. xxi.
Wu, Q., He, X. & Hintz, T. 2005, 'Bi-lateral filtering based edge detection on hexagonal architecture', ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. II713-II716.
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Edge detection plays an important role in image processing area but is still an open problem. This paper presents a novel edge detection method based on bi-lateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on hexagonally sampled images. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained on hexagonal architecture than common edge detection on square architecture. Experimental results using our proposed method in this paper exhibit encouraging performance. © 2005 IEEE.
Abachi, H., Abdullah, R., Ahuja, S.P., Alghazo, J.M., Andresen, D., Arabnia, H.R., Arcangeli, J.P., Astsatryan, H., Chandy, J., Chen, Z., Chiu, S.C., Chung, P.T., Constantinides, C., DeMara, R.F., Dominguez, F.Q., Goswami, D., Gravvanis, G.A., Grosu, D., Guo, M., Sean He, X., Hsieh, S.Y., Joe, K., Kato, H., Kettimuthu, R., Lee, J.R., Lee, J., Li, K.C., Liao, W., Liu, D., Lopez, E.M.M., Lopez-Benitez, N., Lu, T., Marowka, A., McDonald-Maier, K.D., Mun, Y., Nagar, N., Nakamori, M., Oudshoorn, M., Pang, J., Park, J.H., Pedersen, M., Pescape', A., Power, D., Shrikumar, H., Stiles, S., Su, H.C., Subramani, K., Tinetti, F.G., Veselovsky, G., Watson, L.T., Wolfinger, B.E., Woo, J., Xu, B., Yim, K.S., Young, G., Zabir, S., Zhang, C. & Zhang, X. 2005, 'Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'05): Foreword', Proceedings of the 2005 International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA'05.
Liu, D. & He, S. 2004, 'Interframe Compression Algorithm of Block Matching Video Image in Navigated Dynamic Simulation System', Simulation Technology and Applications, Conference on Automatic Simulation Technology and Its Applications, Chinese University of Science and Technology Press, Ningbo, China, pp. 184-188.
Jia, W., He, S. & Piccardi, M. 2004, 'Automatic License Plate Recognition: A review', Proceedings of the International Conference on Imaging Science, Systems and Technology, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, Nevada, USA, pp. 43-48.
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Hintz, T.B., Wang, H. & He, S. 2004, 'Further Approaches to Image Compression on Spiral Architecture', Proceedings of the International Conference on Imaging Science, Systems and Technology, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, Nevada, USA, pp. 57-59.
Xu, H. & He, S. 2004, 'Barriers of IT Diffusion in China', Innovations Through Information Technology, International Conference on Information Resources Management, Idea Group Publishing, New Orleans, Louisana, USA, pp. 811-814.
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He, S., Wang, H., Hintz, T.B. & Wu, Q. 2004, 'How Can Spiral Architecture Improve Image Compression?', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 226-230.
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Liu, D., He, S. & Jin, Y. 2004, 'The Bivariable Fractal Interpolation Algorithm of Simulating the Mountains in the Distributed Navigation Simulation System', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 185-188.
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Wu, Q., He, S. & Hintz, T.B. 2004, 'Preliminary Image Compression Research Using Uniform Image Partitioning on Spiral Architecture', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 216-221.
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Zhou, J., Hu, Q. & He, S. 2004, 'Edge Detection with Bilateral Filtering in Spiral Architecture', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 222-225.
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Jia, W., He, S. & Lin, Q. 2004, 'Echocardiography Sequential Images Compression Based on Region of Interest', Proceedings of 2nd International Conference on Information Technology and Applications, International Conference on Information Technology and Applications, Macquarie Scientific Publishing, Harbin, China, pp. 232-237.
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He, S. & Arabnia, H. 2004, 'Scalable Switch for Bi-Directional MultiRing Network', Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology ISSPIT, International Symposium on Signal Processing and Information Technology, IEEE Computer Society, Rome, Italy, pp. 279-282.
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Jia, W., He, S. & Wu, Q. 2004, 'Edge Analysis on Rectangular and Hexagonal Architectures', Proceedings of the International Conference on Information and Communication Technologies, International Conference on Information and Communication Technologies, Assumption University, Bangkok, Thailand, pp. 69-75.
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Arabnia, H. & He, S. 2004, 'Scalable Switch for Uni-Directional Multi Ring Network', Proceedings of the ISCA 17th International Conference, Parallel and Distributed Computing Systems, International Conference, Parallel and Distributed Computing Systems, ISCA, San Francisco, USA, pp. 302-307.
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Arabnia, H. & He, S. 2004, 'Edge Detection Using Multi Ring on Spiral Architecture', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 413-419.
Zheng, L. & He, S. 2003, 'Edge Detection Based on Modified BP Algorithm of ANN', Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing (VIP2003), Pan-Sydney Area Workshop on Visual Information Processing, Australian Computer Society, Sydney, Australia, pp. 119-122.
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He, S. & Arabnia, H. 2004, 'Parallel Edge Detection Using Uni Directional Multi Ring on Spiral Architecture', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (2004), International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 420-426.
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He, S., Hintz, T.B. & Wu, Q. 2004, 'Edge Detection on Spiral Architecture : An Overview', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (2004), International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 406-412.
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Wang, H., Hintz, T.B., He, S. & Wu, Q. 2004, 'How Pseudo Model May Help Evaluate Image Compression on Spiral Architecture', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, La Vegas, USA, pp. 389-394.
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Liu, D. & He, S. 2004, 'Seamless Image Mosaic for multi-Viewpoint Overlapping Pictures', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 395-398.
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Wu, Q., He, S. & Hintz, T.B. 2004, 'Virtual Spiral Architecture', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, Nevada, USA, pp. 399-405.
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Xu, H. & He, S. 2004, 'Study of the Prioritization of Network Traffic for Wireless Traffic Monitoring Systems', Proceedings of the International Conference on Imaging Science, Systems and Technology, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, USA, pp. 35-38.
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Hu, Q., He, S. & Zhou, J. 2003, 'Multi-Scale Edge Detection with Bilateral Filtering in Spiral Architecture', Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing (VIP2003), Pan-Sydney Area Workshop on Visual Information Processing, ACM Digital Library, Sydney, Australia, pp. 29-32.
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Hintz, T.B., Wang, H. & He, S. 2004, 'Further Approaches to Image Compression on Spiral Architecture', Proceedings of the International Conference on Image Science, Systems and Technology (CISSTâ2004), International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, pp. 57-59.
He, X., Wang, H., Hintz, T. & Wu, Q. 2004, 'How can spiral architecture improve image compression?', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 426-431.
An image file contains a large amount of data. Image compression changes a large image file into a much smaller file. Smaller files require less memory to store, less computer network bandwidth to transfer on the Internet, less time for a computer to process. Image compression becomes important and necessary due to limited computer network bandwidth, computer processor speed and computer storage size. In this paper, we represent an image on our recently created image structure, called Spiral Architecture. We propose algorithms for image compression based on important features of Spiral Architecture: locality and uniformity. Locality is a consequence of a special scheme for numbering image pixels which guarantees physical proximity of the pixels with neighbouring addresses. Uniformity refers to uniformly separating image into similar sub-images.
Liu, D., He, X. & Jin, Y. 2004, 'The bivariable fractal interpolation algorithm of simulating the mountains in the distributed navigation simulation system', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), p. 410.
3D scene system is an important part of computer imitation system, and its fidelity determines if an imitation system stands or falls. At present, such algorithms have been perfected, but a good algorithm is always so complicated that can not achieve the demands of calculation during the time required. So it is necessary to find a fast algorithm applied in the real time system. Fractal Geometry is a powerful tool to describe the complicated and anomalistic geometrical objects. A method of bivariable fractal interpolation combined with objects polyhedral technique to construct polyhedrons and vertex data of mountains in distributed simulation system is proposed. The implementing method and steps are given as well.
Wu, Q., He, X. & Hintz, T. 2004, 'Preliminary image compression research using uniform image partitioning on spiral architecture', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 416-421.
Spiral Architecture is a relatively new and powerful approach to general purpose machine vision system. Using Spiral Multiplication and Spiral Addition, two special mathematical operations on Spiral Architecture, a uniform image partitioning method was proposed earlier. In this paper, preliminary research of image compression based on such a novel image partitioning is presented. It is demonstrated that after uniform image partitioning the sub-images have the properties that pixel intensities between the sub-images are quite similar thus giving opportunities for image compression.
Zhou, J., Hu, Q. & He, X. 2004, 'Edge detection with bilateral filtering in spiral space', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 422-425.
Edge detection is a vital preprocessing step towards high-level Image analysis. One of Its many applications of edge is that It can be used In Image compression where accurate edge detection Is required. A way for Improving the accuracy and quality of edge detection of noisy contaminated image is to preserve edge details while removing noise. In this paper Spiral Architecture Is used to sample image data. Spiral Architecture provides powerful computational power and enables Image to be uniformly partitioned and distributed to various processors for parallel processing. This paper shows the implementation of recently developed bilateral filtering technique in Spiral Architecture for edge-preserving smoothing of noise in images.
Jia, W., He, X. & Lin, Q. 2004, 'Echocardiography sequential images compression based on region of interest', Proceedings of the Second International Conference on Information Technology and Applications (ICITA 2004), pp. 432-437.
This paper describes the studies on methods of compression for medical echocardiography sequence, or called ultrasound sequential images in this paper. The aim is to find a combination of methods, which achieves the highest overall compression performance. Our approach is based on region of interest (ROI), i.e. to segment the image into several regions according to their spatial characteristics, and then compress them separately with different methods. In doing so, we are able to achieve a relatively high compression ratio of about 7.2 white preserving the lossless contents of important regions.
Zheng, L., Li, Y. & He, S. 2002, 'Study on Subpixel Industrial Online Measurement', Proceedings of the 6th International Conference/Exhibition on High Performance Computing in Asia-Pacific Region, International Conference and Exhibition on High Performance Computing in the Asia-Pacific Region, Tata McGraw-Hill Publishing Company Ltd, Bangalore, India, pp. 79-83.
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Wu, Q., He, S., Hintz, T.B. & Ye, Y. 2003, 'Complete Image Partitioning on Spiral Architecture', Lecture Notes in Computer Science - Parallel and Distributed Processing and Applications International Symposium, ISPA 2003, IEEE International Symposium on Parallel and Distributed Processing with Applications, Springer-Verlag, Japan, pp. 304-315.
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Uniform image partitioning has been achieved on Spiral Architecture, which plays an important role in parallel image processing on many aspects such as uniform data partitioning, load balancing, zero data exchange between the processing nodes et al. However, when the number of partitions is not the power of seven like 49, each subimage except one is split into a few fragments which are mixed together. We could not tell which fragments belong to which sub-image. It is an unacceptable flaw to parallel image processing. This paper proposes a method to resolve the problem mentioned above. From the experimental results, it is shown that the proposed method correctly identifies the fragments belonging to the same sub-image and successfully collects them together to be a complete sub-image. Then, these sub-images can be distributed into the different processing nodes for further processing.
He, S., Wu, Q., Liu, D. & Zheng, L. 2003, 'A Distributed and Parallel Edge Detection Scheme within Spiral Architecture', Proceedings of the Third IASTED International Conference on Visualisation, Imaging and Image Processing, IASTED International Conference on Visualisation, Imaging and Image Processing, ACTA Press, Benalmadena, Spain, pp. 371-375.
Wu, Q., He, S. & Hintz, T.B. 2003, 'A Triple-Diagonal Gradient-Based Edge Detection', Proceedings of the 6th IASTED International Conference on Computer Graphics and Imaging, IASTED International Conference on Computer Graphics and Imaging, ACTA Press, Honolulu, USA, pp. 244-249.
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Wu, Q., He, S. & Hintz, T.B. 2003, 'Enhanced Affine Invariant Shape Description by ESPRIT', Proc of the 3rd International Symposium on Image and Signal Processing Analysis, International Symposium on Image and Signal Processing Analysis, Faculty of Engineering and Computing, University of Zagreb, Croatia, Rome, Italy, pp. 593-598.
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He, S., Wu, Q. & Wang, H. 2003, 'Faster and More Accurate Edge Detection on Spiral Architecture', Proceedings of the International Conference on Imaging Science, Systems and Technology, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, USA, pp. 186-191.
Wu, Q., He, S. & Hintz, T.B. 2003, 'Edge Map Improvement on Spiral Architecture', Proceedings of the International Conference on Imaging Science, Systems and Technology, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, USA, pp. 179-185.
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Liu, D., He, S. & Jin, Y. 2003, 'The Bivariable Fractal Algorithm in Distributed Navigational Simulation System', Proc of the International Conference on Imaging Science, Systems and Technology Volume 1, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, USA, pp. 192-195.
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Hu, Q., He, S. & Wu, Q. 2003, 'Concurrent Edge detection with Spiral Architecture on linux', International Conference on Information Technology Coding and Computing ITCC 2003, International Conference on Information Technology: Coding and Computing, IEEE Computer Society, Las Vegas, USA, pp. 524-528.
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Liu, D. & He, X. 2003, 'A kind of video image compression algorithm based on bivariable fractal function space', IASTED International Conference on Computer Graphics and Imaging, pp. 316-319.
Video image compression is the hot topic of image compression and coding. Many useful results of bivariable functions have been obtained by studying the bivariable fractal space. A Video image compression algorithm based on bivariable fractal function has been proposed. The reliable result is available.
Hu, Q., He, S. & Zhou, J. 2002, 'Concurrent Edge Detection Algorithm based on Spiral Architecture Using Linux', Proceedings of International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 34-39.
Wu, Q., He, S. & Hintz, T.B. 2002, 'Distributed Image Processing on Spiral Architecture', Proceedings of the 5th International Conference on Algorithms and Architecture for Parallel Processing, 2002 5th International Conference on Algorithms and Architecture for Parallel Processing, IEEE Computer Society, Beijing, China, pp. 84-91.
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He, S., Hintz, T.B. & Wu, Q. 2002, 'An Overview of Object Recognition on the Spiral Architecture', Proceedings of International Conference on Imaging Science, Systems, and Technology (CISST2002), International Conference Imaging Science, Systems and Technology, CSREA, Las Vegas, pp. 14-20.
Wu, Q., He, S. & Hintz, T.B. 2002, 'Scaling Factor Computation in Image Partitioning on Spiral Architecture', Proc. International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, US, pp. 28-33.
He, S., Hintz, T.B. & Wu, Q. 2002, 'Neural Network Based Image Edge Detection Within Spiral Architecture', Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 21-27.
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Lin, A. & He, S. 2002, 'Monitor Distributed Image Recognition Environment Using Intelligent Agents', Proceedings of International Conference on Parallel and Distributed Processing Techniques and Applications, International Conference on Parallel and Distributed Processing Techniques and Applications, CSREA Press, Las Vegas, USA, pp. 14-20.
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He, S., Hintz, T.B. & Wu, Q. 2001, 'A skeleton Algorithm on Clusters for Image Edge Detection', Proceedings 15th International Parallel & Distributed Processing Symposium 2001, IEEE International Parallel and Distributed Processing Symposium (was IPPS and SPDP), IEEE Computer Society, San Francisco, USA.
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He, S., Wu, Q. & Hintz, T.B. 2001, 'Spiral Object Recognition on Clusters', Proceedings of the International Conference on Image Science, Systems, and Technology CISST'2001, International Conference Imaging Science, Systems and Technology, CSREA Press, Las Vegas, USA, pp. 605-611.
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Wu, Q., Hintz, T.B. & He, S. 2001, 'Image Edge Detection in a Mimre Spiral Architecture', Proceedings of The 9th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2001, International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, University of West Bohemia, Plzen, Czech Republic, pp. 320-327.
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He, S. & Hintz, T.B. 2000, 'Parallel Recognition of Occluded Objects within Spiral Architecture', 18th IASTED International Conference on Applied Informatics, IASTED/ACTA Press, Innsbruck, Austria, pp. 285-289.
He, S. & Hintz, T.B. 2000, 'Refining Occluded Object Recognition within Spiral Architecture', 2000 International Conference on Image Science, Systems and Applications, CSREA Press, Las Vegas, Nevada, USA, pp. 701-708.
He, S., Hintz, T.B. & Szewcow, U. 2000, 'Object Recognition by an Integral Invariant within Spiral Architecture', Fourth Asian Conference on Computer Vision, Asian Conference on Computer Vision, N/A, Taipei, Taiwan, pp. 294-299.
He, S. & Hintz, T.B. 2000, 'Refining Edge Detection within Spiral Architecture', 23rd Australasian Computer Science Conference, Australasian Computer Science Conference, IEEE Computre Society, Canberra, Australia, pp. 113-119.

Journal articles

Zhang, T., Yang, Z., Jia, W., Yang, B., Yang, J. & He, X. 2016, 'A new method for violence detection in surveillance scenes', Multimedia Tools and Applications, vol. 75, no. 12, pp. 7327-7349.
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Violence detection is a hot topic for surveillance systems. However, it has not been studied as much as for action recognition. Existing vision-based methods mainly concentrate on violence detection and make little effort to determine the location of violence. In this paper, we propose a fast and robust framework for detecting and localizing violence in surveillance scenes. For this purpose, a Gaussian Model of Optical Flow (GMOF) is proposed to extract candidate violence regions, which are adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. Violence detection is then performed on each video volume constructed by densely sampling the candidate violence regions. To distinguish violent events from nonviolent events, we also propose a novel descriptor, named as Orientation Histogram of Optical Flow (OHOF), which are fed into a linear SVM for classification. Experimental results on several benchmark datasets have demonstrated the superiority of our proposed method over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.
Chen, Y., Wang, J., Xu, M., He, X. & Lu, H. 2016, 'A unified model sharing framework for moving object detection', Signal Processing, vol. 124, pp. 72-80.
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Millions of surveillance cameras have been installed in public areas, producing vast amounts of video data every day. It is an urgent need to develop intelligent techniques to automatically detect and segment moving objects which have wide applications. Various approaches have been developed for moving object detection based on background modeling in the literature. Most of them focus on temporal information but partly or totally ignore spatial information, bringing about sensitivity to noise and background motion. In this paper, we propose a unified model sharing framework for moving object detection. To begin with, to exploit the spatial-temporal correlation across different pixels, we establish a many-to-one correspondence by model sharing between pixels, and a pixel is labeled into foreground or background by searching an optimal matched model in the neighborhood. Then a random sampling strategy is introduced for online update of the shared models. In this way, we can reduce the total number of models dramatically and match a proper model for each pixel accurately. Furthermore, existing approaches can be naturally embedded into the proposed sharing framework. Two popular approaches, statistical model and sample consensus model, are used to verify the effectiveness. Experiments and comparisons on ChangeDetection benchmark 2014 demonstrate the superiority of the model sharing solution.
Zhang, T., Jia, W., Yang, B., Yang, J., He, X. & Zheng, Z. 2016, 'MoWLD: a robust motion image descriptor for violence detection', Multimedia Tools and Applications, pp. 1-20.
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© 2015 Springer Science+Business Media New York Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts.
Chua, T.S., He, X., Liu, W., Piccardi, M., Wen, Y. & Tao, D. 2016, 'Big data meets multimedia analytics', Signal Processing, vol. 124, pp. 1-4.
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Usman, M., He, X., Lam, K., Xu, M., Bokhari, S.M. & Chen, J. 2016, 'Frame Interpolation for Cloud-Based Mobile Video Streaming', IEEE Transactions on Multimedia.
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Ambusaidi, M., He, X., Nanda, P. & Tan, Z. 2016, 'Building an intrusion detection system using a filter-based feature selection algorithm', IEEE Transactions on Computers, pp. 1-1.
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Li, W., Li, H., Dong, T., Yao, J. & He, X. 2016, 'Traffic sign localization based on edge-color pair and feature filters', ICIC Express Letters, vol. 10, no. 3, pp. 727-732.
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© 2016, ICIC International. Traffic sign localization is a key part of the traffic sign recognition system. A novel algorithm for traffic sign localization based on edge-color pair combined with feature filters is presented in this paper. Firstly, the color edge detection is taken by the method of distance measure of direction region in a color image. Secondly, the suspected regions of the traffic sign are localized roughly based on edge-color pair. Finally, a two-level feature filter is designed to localize the traffic sign accurately. The experiment on 200 traffic sign images that were taken under various conditions shows the extraction rate of 96.1%. The experimental results show that the algorithm in this paper can effectively eliminate the color fade effect and the analogue interference effect on traffc sign localization.
Zhou, H.L., Lam, K.M. & He, X.S. 2016, 'Shape-Appearance-Correlated Active Appearance Model Pattern Recognition', Pattern Recognition.
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Chomsiri, T., He, X.S., Nanda, P. & Tan, Z. 2016, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Zhao, Y., He, X., Chen, B. & Zhao, X. 2016, 'Enhanced kernel minimum squared error algorithm and its application in face recognition', Journal of Southeast University (English Edition), vol. 32, no. 1, pp. 35-38.
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© 2016, Editorial Department of Journal of Southeast University. All right reserved. To improve the classification performance of the kernel minimum squared error (KMSE), an enhanced KMSE algorithm (EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix. Compared with the common methods, the new objective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE, some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification (CRC).
Jan, M.A., Nanda, He, X.S. & Liu, R.P. 2016, 'A Sybil Attack Detection Scheme for a Forest Wildfire Monitoring Application', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.
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Zhang, T., Jia, W., He, X.S. & Yang, J. 2016, 'Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection', IEEE Transactions on Circuits and Systems for Video Technology.
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Dai, M., Cheng, S. & He, X.S. 2016, 'Hybrid generative–discriminative hash tracking with spatio-temporal contextual cues', Neural Computing and Applications.
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Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified particle filter with hash fingerprints for the coarse matching by the maximum a posteriori and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because hash fingerprint is merely a binary vector and the modified particle filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on eight challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms eight state-of-the-art trackers in terms of both accuracy and speed.
Usman, M., Jan, M.A. & He, X.S. 2016, 'Cryptography-Based Secure Data Storage and Sharing Using HEVC and Public Clouds', Information Sciences.
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Mobile devices are widely used for uploading/downloading media files such as audio, video and images to/from the remote servers. These devices have limited resources and are required to offload resource-consuming media processing tasks to the clouds for further processing. Migration of these tasks means that the media services provided by the clouds need to be authentic and trusted by the mobile users. The existing schemes for secure exchange of media files between the mobile devices and the clouds have limitations in terms of memory support, processing load, battery power, and data size. These schemes lack the support for large-sized video files and are not suitable for resource-constrained mobile devices. This paper proposes a secure, lightweight, robust and efficient scheme for data exchange between the mobile users and the media clouds. The proposed scheme considers High Efficiency Video Coding (HEVC) Intra-encoded video streams in unsliced mode as a source for data hiding. Our proposed scheme aims to support real-time processing with power-saving constraint in mind. Advanced Encryption Standard (AES) is used as a base encryption technique by our proposed scheme. The simulation results clearly show that the proposed scheme outperforms AES-256 by decreasing the processing time up to 4.76% and increasing the data size up to 0.72% approximately. The proposed scheme can readily be applied to real-time cloud media streaming.
Hasan, M.A., Xu, M., He, X. & Wang, Y. 2015, 'A camera motion histogram descriptor for video shot classification', Multimedia Tools and Applications, vol. 74, no. 24, pp. 11073-11098.
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© 2014, Springer Science+Business Media New York. In this paper, a novel camera motion descriptor is proposed for video shot classification. In the proposed method, raw motion information of consecutive video frames are extracted by computing the motion vector of each macroblock to form motion vector fields (MVFs). Next, a motion consistency analysis is applied on MVFs to eliminate the inconsistent motion vectors. Then, MVFs are divided into nine (3 3) local regions and the singular value decomposition (SVD) technique is applied on the motion vectors extracted from each local region in the temporal direction. Consistent motion vectors of a number of MVFs are compactly represented at a time to characterize temporal camera motion. Accordingly, each local region of the whole video shot is represented using a sequence of compactly represented vectors. Finally, the sequence of vectors is converted into a histogram to describe the camera motions of each local region. Combination of all the local histograms is considered as the camera motion descriptor of a video shot. The shot descriptors are used in a classifier to classify video shots. In this work, we use support vector machine (SVM) for performing classification tasks. The experimental results show that the proposed camera motion descriptor has strong discriminative capability to classify different camera motion patterns in professionally captured video shots effectively. We also show that our proposed approach outperforms two state-of-the-art video shot classification methods.
Wang, Y., Liu, J., Fan, X., He, X., Jia, Q. & Gao, R. 2015, 'Online gesture-based interaction with visual oriental characters based on manifold learning', Signal Processing, vol. 110, pp. 123-131.
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© 2014 Elsevier B.V. Online gesture-based interaction with characters has become a more natural and informative human-computer interface with the popularity of new interactive devices (e.g., Kinect and Leap Motion). In this paper, a new feature descriptor named Segmented Directed-edge Vector (SDV) is proposed. This simple and yet quite effective descriptor is able to capture the characteristics of visual oriental characters. Moreover, we explicitly build the mappings from SDVs to features in a subspace by a modified Locality Preserving Projections (LPP) method with stroke class constraints. These mappings can yield meaningful subspace structures for larger character sets. Extensive experiments on the online interactive system demonstrate the robustness of our method to various issues in gesture-based characters input, such as unnatural breaks, overlapped or distorted radicals, and unconscious or quivering trajectories. Our system can still achieve accurate recognition when accumulative errors occur with complex characters.
Luo, X., Wan, Y., He, X. & Mori, K. 2015, 'Adaptive marker-free registration using a multiple point strategy for real-time and robust endoscope electromagnetic navigation.', Computer methods and programs in biomedicine, vol. 118, no. 2, pp. 147-157.
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Registration of pre-clinical images to physical space is indispensable for computer-assisted endoscopic interventions in operating rooms. Electromagnetically navigated endoscopic interventions are increasingly performed at current diagnoses and treatments. Such interventions use an electromagnetic tracker with a miniature sensor that is usually attached at an endoscope distal tip to real time track endoscope movements in a pre-clinical image space. Spatial alignment between the electromagnetic tracker (or sensor) and pre-clinical images must be performed to navigate the endoscope to target regions. This paper proposes an adaptive marker-free registration method that uses a multiple point selection strategy. This method seeks to address an assumption that the endoscope is operated along the centerline of an intraluminal organ which is easily violated during interventions. We introduce an adaptive strategy that generates multiple points in terms of sensor measurements and endoscope tip center calibration. From these generated points, we adaptively choose the optimal point, which is the closest to its assigned the centerline of the hollow organ, to perform registration. The experimental results demonstrate that our proposed adaptive strategy significantly reduced the target registration error from 5.32 to 2.59 mm in static phantoms validation, as well as from at least 7.58 mm to 4.71 mm in dynamic phantom validation compared to current available methods.
Gong, C., Fu, K., Zhou, L., Yang, J. & He, X. 2015, 'Scalable Semi-Supervised Classification via Neumann Series', NEURAL PROCESSING LETTERS, vol. 42, no. 1, pp. 187-197.
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Luo, X., Wan, Y., He, X. & Mori, K. 2015, 'Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking.', Medical image analysis, vol. 24, no. 1, pp. 282-296.
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This paper proposes an observation-driven adaptive differential evolution algorithm that fuses bronchoscopic video sequences, electromagnetic sensor measurements, and computed tomography images for accurate and smooth bronchoscope three-dimensional motion tracking. Currently an electromagnetic tracker with a position sensor fixed at the bronchoscope tip is commonly used to estimate bronchoscope movements. The large tracking error from directly using sensor measurements, which may be deteriorated heavily by patient respiratory motion and the magnetic field distortion of the tracker, limits clinical applications. How to effectively use sensor measurements for precise and stable bronchoscope electromagnetic tracking remains challenging. We here exploit an observation-driven adaptive differential evolution framework to address such a challenge and boost the tracking accuracy and smoothness. In our framework, two advantageous points are distinguished from other adaptive differential evolution methods: (1) the current observation including sensor measurements and bronchoscopic video images is used in the mutation equation and the fitness computation, respectively and (2) the mutation factor and the crossover rate are determined adaptively on the basis of the current image observation. The experimental results demonstrate that our framework provides much more accurate and smooth bronchoscope tracking than the state-of-the-art methods. Our approach reduces the tracking error from 3.96 to 2.89 mm, improves the tracking smoothness from 4.08 to 1.62 mm, and increases the visual quality from 0.707 to 0.741.
Zhou, T., He, X., Xie, K., Fu, K., Zhang, J. & Yang, J. 2015, 'Robust visual tracking via efficient manifold ranking with low-dimensional compressive features', Pattern Recognition, vol. 48, no. 8, pp. 2459-2473.
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© 2015 Elsevier Ltd. All rights reserved. Abstract In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes. The goal of tracking is to search the unlabeled sample that is the most relevant to the existing labeled nodes. Therefore, visual tracking is regarded as a ranking problem in which the relevance between an object appearance model and candidate samples is predicted by the manifold ranking algorithm. Due to the outstanding ability of the manifold ranking algorithm in discovering the underlying geometrical structure of a given image database, our tracker is more robust to overcome tracking drift. Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features for object representation. Furthermore, spatial context is used to improve the robustness to appearance variations. Experimental results on some challenging video sequences show that the proposed algorithm outperforms seven state-of-the-art methods in terms of accuracy and robustness.
Tan, Z., Jamdagni, A., He, X., Nanda, P., Liu, R.P. & Hu, J. 2015, 'Detection of Denial-of-Service Attacks Based on Computer Vision Techniques', IEEE Transactions on Computers, vol. 64, no. 9, pp. 2519-2533.
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Wang, S., Wu, Q., He, X., Yang, J. & Wang, Y. 2015, 'Local N-ary Pattern and Its Extension for Texture Classication', IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 9, pp. 1495-1506.
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Texture image classification is important in computer vision research. To effectively capture texture patterns, a distinctive feature such as a local binary pattern (LBP) is needed. An LBP is robust against monotonic and gray-scale variations and it computes quickly. Its robustness and speed advantage have made it popular in various texture analysis applications. However, an LBP is sensitive to noise, particularly smooth weak illumination gradients in near-uniform regions. To mitigate the effect of noise and increase distinctiveness, a local ternary pattern (LTP) is proposed. Compared with a binary coding LBP, an LTP adopts ternary coding. As a result, an LTP can better tolerate noise and is significantly more distinctive. These advantages of an LTP effectively improve its classification accuracy. However, the potential of ternary coding is not fully explored in LTPs because a ternary pattern is split into a pair of binary patterns. In this paper, to fully explore the distinctiveness in the local pattern, the feature extraction process is formulated as an integer decomposition problem, which is a generalized version of the Bachet de Meziriac weight problem (BMWP). Following this generalization, a local n -ary pattern (LNP) is proposed, for which the LBP is a special case parametrized under n=2 . The LTP is not a special case of the LNP. Both LBP and LTP are used as benchmark methods to evaluate LNPs performance due to their well-recognized success. In addition, a rotation-invariant and uniform LNP is also proposed and compared with a rotation-invariant and uniform LBP. The proposed LNP achieves significantly improved texture classification accuracy compared with the LBP and also demonstrates considerable improvement over the LTP.
Zhou, T., Xie, K., Zhang, J., Yang, J. & He, X. 2015, 'Robust object tracking based on weighted subspace reconstruction error with forward: Backward tracking criterion', Journal of Electronic Imaging, vol. 24, no. 3.
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© 2015 SPIE and IS&T. It is a challenging task to develop an effective and robust object tracking method due to factors such as severe occlusion, background clutters, abrupt motion, illumination variation, and so on. A tracking algorithm based on weighted subspace reconstruction error is proposed. The discriminative weights are defined based on minimizing reconstruction error with a positive dictionary while maximizing reconstruction error with a negative dictionary. Then a confidence map for candidates is computed through the subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which combines the discriminative weights and subspace reconstruction error. Furthermore, the new evaluation method based on a forward-backward tracking criterion is used to verify the proposed method and demonstrates its robustness in the updating stage and its effectiveness in the reduction of accumulated errors. Experimental results on 12 challenging video sequences show that the proposed algorithm performs favorably against 12 state-of-the-art methods in terms of accuracy and robustness.
He, X., Luo, S., Tao, D., Xu, C. & Yang, J. 2015, 'The 21st International Conference on MultiMedia Modeling', IEEE Multimedia, vol. 22, no. 2, pp. 86-88.
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© 2015 IEEE. This report on The 21st International Conference on MultiMedia Modeling provides an overview of the best papers and keynote presentations. It also reviews the special sessions on Personal (Big) Data Modeling for Information Access and Retrieval; Social Geo-Media Analytics and Retrieval; and Image or Video Processing, Semantic Analysis, and Understanding.
Luo, X., Wan, Y. & He, X. 2015, 'Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.', Medical physics, vol. 42, no. 4, pp. 1808-1817.
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Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization.The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm.The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29.A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimi...
He, X., Chomsiri, T., Nanda, P. & Tan, Z. 2014, 'Improving Cloud Network Security using the Tree-Rule Firewall', Future Generation Computer Systems, vol. 30, pp. 116-126.
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Tan, Z., Jamdagni, A., He, X., Nanda, P. & Liu, R.P. 2014, 'A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis', IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 2, pp. 447-456.
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Wang, J., Xu, M., He, X., Lu, H. & Hoang, D.B. 2014, 'A hybrid domain enhanced framework for video retargeting with spatialtemporal importance and 3D grid optimization', Signal Processing, vol. 94, pp. 33-47.
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Recently, a ubiquitous video access is highly demanded for online video applications. One big challenge is that video service needs to adapt different device capabilities. Pervasive multimedia devices require an accurate and user comfort video retargeting. Letting users see their preferred content accurately directly affects their comforts. User preferences on video contents are different in various video domains. In this paper, we present a hybrid framework of video retargeting with a domain enhanced spatial-temporal grid optimisation. First, we parse videos from low-level features to high-level visual concepts, combining with visual attention for an accurate importance description. Second, a semantic importance map is built up representing the spatial importance and temporal continuity, which is incorporated with a 3D rectilinear grid scaleplate to map frames to a target display, thereby keeping the aspect ratio of semantically salient objects as well as the perceptual coherency. Extensive evaluations are made on five typical video genres, i.e. sports, advertisements, lecture, news and surveillance. The comparison with the state-of-the-art approaches on both images and videos have demonstrated the advantages of the proposed approach.
Zhang, T., Yang, Z., Jia, W., Wu, Q., Yang, J. & He, S. 2014, 'Fast and robust head detection with arbitrary pose and occlusion', Multimedia Tools And Applications, vol. 74, no. 21, pp. 9365-9385.
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Head detection in images and videos plays an important role in a wide range of computer vision and surveillance applications. Aiming to detect heads with arbitrarily occluded faces and head pose, in this paper, we propose a novel Gaussian energy function based algorithm for elliptical head contour detection. Starting with the localization of head and shoulder by an improved Gaussian Mixture Model (GMM) approach, the precise head contour is obtained by making use of the Omega shape formed from the head and shoulder. Experimental results on several benchmark datasets demonstrate the superiority of the proposed idea over the state-of-the-art in both detection accuracy and processing speed, even though there are various types of severe occlusions in faces.
Jan, M.A., Nanda, P., He, X. & Liu, R.P. 2014, 'PASCCC: Priority-based application-specific congestion control clustering protocol', Computer Networks, vol. 74, no. B, pp. 92-102.
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Wireless sensor networks comprise resource-starved sensor nodes, which are deployed to sense the environment, gather data, and transmit it to a base station (BS) for further processing. Cluster-based hierarchical-routing protocols are used to efficiently utilize the limited energy of the nodes by organizing them into clusters. Only cluster head (CH) nodes are eligible for gathering data in each cluster and transmitting it to a BS. Unbalanced clusters result in network congestion, thereby causing delay, packet loss, and degradation of Quality of Service (QoS) metrics. In this study, we propose a priority-based application-specific congestion control clustering (PASCCC) protocol, which integrates the mobility and heterogeneity of the nodes to detect congestion in a network. PASCCC decreases the duty cycle of each node by maintaining threshold levels for various applications. The transmitter of a sensor node is triggered when the reading of a specific captured event exceeds a specific threshold level. Time-critical packets are prioritized during congestion in order to maintain their timeliness requirements. In our proposed approach, CHs ensure coverage fidelity by prioritizing the packets of distant nodes over those of nearby nodes. A novel queue scheduling mechanism is proposed for CHs to achieve coverage fidelity, which ensures that the extra resources consumed by distant nodes are utilized effectively. The effectiveness of PASCCC was evaluated based on comparisons with existing clustering protocols. The experimental results demonstrated that PASCCC achieved better performance in terms of the network lifetime, energy consumption, data transmission, and other QoS metrics compared with existing approaches.
Zeng, C., Jia, W., He, X. & Zhang, L. 2014, 'Text Detection in Born-Digital Images using IT-LBP', Journal of Algorithms & Computational Technology, vol. 8, no. 1, pp. 127-142.
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Ambusaidi, M.A., Tan, Z., He, X., Nanda, P., Lu, L.F. & Jamdagni, A. 2014, 'Intrusion detection method based on nonlinear correlation measure', International Journal of Internet Protocol Technology, vol. 8, no. 2/3, pp. 77-86.
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Cyber crimes and malicious network activities have posed serious threats to the entire internet and its users. This issue is becoming more critical, as network-based services, are more widespread and closely related to our daily life. Thus, it has raised a serious concern in individual internet users, industry and research community. A significant amount of work has been conducted to develop intelligent anomaly-based intrusion detection systems (IDSs) to address this issue. However, one technical challenge, namely reducing false alarm, has been along with the development of anomaly-based IDSs since 1990s. In this paper, we provide a solution to this challenge. A nonlinear correlation coefficient-based (NCC) similarity measure is proposed to help extract both linear and nonlinear correlations between network traffic records. This extracted correlative information is used in our proposed IDS to detect malicious network behaviours. The effectiveness of the proposed NCC-based measure and the proposed IDS are evaluated using NSL-KDD dataset. The evaluation results demonstrate that the proposed NCC-based measure not only helps reduce false alarm rate, but also helps discriminate normal and abnormal behaviours efficiently.
Tan, Z., Nagar, U., He, X., Nanda, P. & Liu, R. 2014, 'Enhancing Big Data Security with Collaborative Intrusion Detection', IEEE Cloud Computing Magazine, pp. 34-40.
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As an asset of Cloud computing, big data is now changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. Its security and privacy, however, have always been a concern of the owners of the data. The security and privacy could be strengthened via securing Cloud computing environments. This requires a comprehensive security solution from attack prevention to attack detection. Intrusion Detection Systems (IDSs) are playing an increasingly important role within the realm of a set of network security schemes. In this article, we study the vulnerabilities in Cloud computing and propose a collaborative IDS framework to enhance the security and privacy of big data.
Hasan, M.A., Xu, M., He, X. & Xu, C. 2014, 'CAMHID: Camera Motion Histogram Descriptor and Its Application to Cinematographic Shot Classification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 10, pp. 1682-1695.
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Zhou, L., Qiao, Y., Li, Y., He, X. & Yang, J. 2014, 'Interactive segmentation based on iterative learning for multiple-feature fusion', Neurocomputing, vol. 135, pp. 240-252.
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Zhou, L., Fu, K., Li, Y., Qiao, Y., He, X. & Yang, J. 2014, 'Bayesian salient object detection based on saliency driven clustering', Signal Processing: Image Communication, vol. 29, no. 3, pp. 434-447.
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Lin, Z., Jiang, Y., Tang, Q. & He, X. 2014, 'Does High-Quality Financial Reporting Mitigate the Negative Impact of Global Financial Crises on Firm Performance? Evidence from the United Kingdom', Australasian Accounting, Business and Finance Journal, vol. 8, no. 5, pp. 19-46.
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Tan, Z., Nagar, U.T., He, X., Nanda, P., Liu, R.P., Wang, S. & Hu, J. 2014, 'Enhancing big data security with collaborative intrusion detection', IEEE Cloud Computing, vol. 1, no. 3, pp. 27-33.
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© 2014 IEEE. Big data, often stored in cloud networks, is changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. The security and privacy of this data, however, have always been a concern of the data owners. Securing cloud computing environments could strengthen data security and privacy. Doing so requires a comprehensive security solution, from attack prevention to attack detection. Intrusion detection systems (IDSs) are playing an increasingly important role in network security schemes. This article studies vulnerabilities in cloud computing and proposes a collaborative IDS framework to enhance the security and privacy of big data.
Xu, M., Wang, J., He, X., Jin, J.S., Luo, S. & Lu, H. 2014, 'A three-level framework for affective content analysis and its case studies', Multimedia Tools and Applications, vol. 70, no. 2, pp. 757-779.
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Emotional factors directly reflect audiences' attention, evaluation and memory. Recently, video affective content analysis attracts more and more research efforts. Most of the existing methods map low-level affective features directly to emotions by applying machine learning. Compared to human perception process, there is actually a gap between low-level features and high-level human perception of emotion. In order to bridge the gap, we propose a three-level affective content analysis framework by introducing mid-level representation to indicate dialog, audio emotional events (e.g., horror sounds and laughters) and textual concepts (e.g., informative keywords). Mid-level representation is obtained from machine learning on low-level features and used to infer high-level affective content. We further apply the proposed framework and focus on a number of case studies. Audio emotional event, dialog and subtitle are studied to assist affective content detection in different video domains/genres. Multiple modalities are considered for affective analysis, since different modality has its own merit to evoke emotions. Experimental results shows the proposed framework is effective and efficient for affective content analysis. Audio emotional event, dialog and subtitle are promising mid-level representations. © 2012 Springer Science+Business Media, LLC.
Ma, J., Zheng, L., Dong, M., He, S., Guo, M., Yaguchi, Y. & Oka, R. 2013, 'A segmentation-free method for image classification based on pixel-wise matching', Journal of Computer and System Sciences, vol. 79, pp. 256-268.
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Categorical classification for real-world images is a typical problem in the field of computer vision. This task is extremely easy for a human due to our visual cortex systems. However, developing a similarity recognition model for computer is still a difficult issue. Although numerous approaches have been proposed for solving the tough issue, little attention is given to the pixel-wise techniques for recognition and classification. In this paper, we present an innovative method for recognizing real-world images based on pixel matching between images. A method called two-dimensional continuous dynamic programming (2DCDP) is adopted to optimally capture the corresponding pixels within nonlinearly matched areas in an input image and a reference image representing an object without advance segmentation procedure. Direction pattern (a set of scalar patterns based on quantization of vector angles) is made by using a vector field constructed by the matching pixels between a reference image and an input image. Finally, the category of the test image is deemed to be that which has the strongest correlation with the orientation patterns of the input image and its reference image. Experimental results show that the proposed method achieves a competitive and robust performance on the Caltech 101 image dataset.
Yu, D., Nanda, P., Cao, L. & He, S. 2013, 'TCTM: an evaluation framework for architecture design on wireless sensor networks', International Journal of Sensor Networks, vol. 14, no. 3, pp. 168-177.
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This paper presents an evaluation framework for architecture designs on wireless sensor networks (WSNs). We introduce a simple evaluation model: triangular constraint tradeoffs model (TCTM) to grasp the essence of the architecture design consideration under transient wireless media characteristic and stringent limitation on energy and computing resource of WSNs. Based on this evaluation framework, we investigate the existing architectures proposed in literature from three main competing constraint aspects, namely generality, cost, and performance. Two important concepts: performance efficiency and deployment efficiency are identified and distinguished in overall architecture efficiency. With this powerful abstract and simple model, we describe the motivations of major body of WSNs architectures proposed in current literature. We also analyse the fundamental advantage and limitations of each class of architectures from TCTM perspective. We foresee the influence of evolving technology to futuristic architecture design. We believe our efforts will serve as a reference to orient researchers and system designers in this area
Yu, D., Nanda, P. & He, X.S. 2013, 'Wireless Sensor Network (WSN) Energy Efficiency Challenge from Implementation Perspectives', Advanced Science Letters, vol. 19, no. 2, pp. 642-645.
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Through review of current Wireless Sensor Network (WSN) energy preserving techniques used in industry and academic research, we recognize that the integration of various techniques through implementation is a challenging task due to application specific nature of system integration. On one hand, most researches on energy efficiency focus on one single layer with perfect assumptions about other layers and environment parameters. While this methodology will simplify the design process and provide valuable insight into single layer solution, such approach cannot provide information on layer incompatibilities between different sets of protocols, nor will give information on the overall performance of a network based on the protocols under test. Further more, under various non-standard assumptions, the real contribution of these proposed optimization methods are difficult to be achieved if not impossible. Hence industry professionals become very cautious to integrate diverse and advance ad hoc solutions into their products and standards. To show credibility of the ad hoc solutions and their implications on industry applications, researchers have to evaluate their solutions under a generic architecture which can test different scenarios and evaluate performance based on a wide range of metrics.
Jamdagni, A., Tan, T., He, S., Nanda, P. & Liu, R. 2013, 'RePIDS: A multi tier Real-time Payload-based Intrusion Detection System', Computer Networks, vol. 57, no. 3, pp. 811-824.
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Intrusion Detection System (IDS) deals with huge amount of network traffic and uses large feature set to discriminate normal pattern and intrusive pattern. However, most of existing systems lack the ability to process data for real-time anomaly detection. In this paper, we propose a 3-Tier Iterative Feature Selection Engine (IFSEng) for feature subspace selection. Principal Component Analysis (PCA) technique is used for the pre-processing of data. Mahalanobis Distance Map (MDM) is used to discover hidden correlations between the features and between the packets. We also propose a novel Real-time Payload-based Intrusion Detection System (RePIDS) that integrates a 3-Tier IFSEng and the MDM approach. Mahalanobis Distance (MD) dissimilarity criterion is used to classify each packet as either a normal or an attack packet. The effectiveness of the proposed RePIDS is evaluated using DARPA 99 dataset and Georgia Institute of Technology attack dataset. The traffic for Web-based application is considered for validating our model. F-value, a criterion, is used to evaluate the detection performance of RePIDS. Experimental results show that RePIDS achieves better performance (high F-values, 0.9958 for DARPA 99 dataset and 0.976 for Georgia Institute of Technology attack dataset respectively, with only 0.85% false alarm rate) and lower computational complexity when compared against two state-of-the-art payload-based intrusion detection systems. Additionally, it has 1.3 time higher throughput in comparison with real scenario of medium sized enterprise network.
Xu, M., Xu, C., He, S., Jin, J., Luo, S. & Rui, Y. 2013, 'Hierarchical affective content analysis in arousal and valence dimensions', Signal Processing, vol. 93, pp. 2140-2150.
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Different from the existing work focusing on emotion type detection, the proposed approach in this paper provides flexibility for users to pick up their favorite affective content by choosing either emotion intensity levels or emotion types. Specifically, we propose a hierarchical structure for movie emotions and analyze emotion intensity and emotion type by using arousal and valence related features hierarchically. Firstly, three emotion intensity levels are detected by using fuzzy c-mean clustering on arousal features. Fuzzy clustering provides a mathematical model to represent vagueness, which is close to human perception. Then, valence related features are used to detect five emotion types. Considering video is continuous time series data and the occurrence of a certain emotion is affected by recent emotional history, conditional random fields (CRFs) are used to capture the context information. Outperforming Hidden Markov Model, CRF relaxes the independence assumption for states required by HMM and avoids bias problem. Experimental results show that CRF-based hierarchical method outperforms the one-step method on emotion type detection. User study shows that majority of the viewers prefer to have option of accessing movie content by emotion intensity levels. Majority of the users are satisfied with the proposed emotion detection.
Bergmann, N., Yeh, W. & He, S. 2013, 'Using Swarm Intelligence to Optimize the Energy Consumption for Distributed Systems', Modern Applied Science, vol. 7, no. 6, pp. 59-66.
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Large, distributed, network-based computing systems (also known as Cloud Computing) have recently gained significant interest. We expect significantly more applications or web services will be relying on network-based servers, therefore reducing the energy consumption of these systems would be beneficial for companies to save their budgets on running their machines as well as cooling down their infrastructures. Dynamic Voltage Scaling can save significant energy for these systems, but it faces the challenge of efficient and balanced parallelization of tasks in order to maximize energy savings while maintaining desired performance levels. This paper proposes our Simplified Swarm Optimization (SSO) method to reduce the energy consumption for distributed systems with Dynamic Voltage Scaling. The results of SSO have been compared to the most popular evolutionary Particle Swarm Optimization (PSO) algorithm and have shown to be more efficient and effective, reducing both the execution time for scheduling and makespan.
Zheng, L., He, S., Samali, B. & Yang, L.T. 2013, 'An Algorithm for Accuracy Enhancement of License Plate Recognition', Journal of Computer and System Sciences, vol. 79, pp. 245-255.
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This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video
Jan, M.A., Nanda, P. & He, S. 2013, 'Energy Evaluation Model for an Improved Centralized Clustering Hierarchical Algorithm in WSN', Lecture Notes in Computer Science, vol. 7889, pp. 154-167.
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Wireless Sensor Networks (WSN) consists of battery-powered sensor nodes which collect data and route the data to the Base Station. Centralized Cluster-based routing protocols efficiently utilize limited energy of the nodes by selecting Cluster Heads (CHs) in each round. Selection of CHs and Cluster formation is performed by the Base Station. In each round, nodes transmit their location information and their residual energy to the Base Station. This operation is a considerable burden on these resource hungry sensor nodes. In this paper we propose a scheme whereby a small number of High-Energy nodes gather location information and residual energy status of the sensing nodes and transmit to the Base Station. This scheme eliminates CH advertisement phase in order to conserve energy. Based on the energy consumption by various types of nodes, we have derived an energy model for our algorithm which depicts the total energy consumption in the network.
Du, R., Wu, Q., He, S. & Yang, J. 2013, 'MIL-SKDE: Multiple-instance learning with supervised kernel density estimation', Signal Processing, vol. 93, no. 6, pp. 1471-1484.
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Multiple-instance learning (MIL) is a variation on supervised learning. Instead of receiving a set of labeled instances, the learner receives a set of bags that are labeled. Each bag contains many instances. The aim of MIL is to classify new bags or instances. In this work, we propose a novel algorithm, MIL-SKDE (multiple-instance learning with supervised kernel density estimation), which addresses MIL problem through an extended framework of KDE (kernel density estimation)þmean shift. Since the KDEþmean shift framework is an unsupervised learning method, we extend KDE to its supervised version, called supervised KDE (SKDE), by considering class labels of samples. To seek the modes (local maxima) of SKDE, we also extend mean shift to a supervised version by taking into account sample labels. SKDE is an alternative of the well-known diverse density estimation (DDE) whose modes are called concepts. Comparing to DDE, SKDE is more convenient to learn multi-modal concepts and robust to labeling noise (mistakenly labeled bags). Finally, each bag is mapped into a concept space where the multi-class SVM classifiers are learned. Experimental results demonstrate that our approach outperforms the state-of-the-art MIL approaches
Gong, C., Fu, K., Tu, E., Yang, J. & He, X. 2013, 'Robust object tracking using linear neighborhood propagation', Journal of Electronic Imaging, vol. 22, no. 1, pp. 013015-1-013015-10.
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An image watermarking scheme that combines Hermite functions expansion and space/spatial-frequency analysis is proposed. In the first step, the Hermite functions expansion is employed to select busy regions for watermark embedding. In the second step, the space/spatial-frequency representation and Hermite functions expansion are combined to design the imperceptible watermark, using the host local frequency content. The Hermite expansion has been done by using the fast Hermite projection method. Recursive realization of Hermite functions significantly speeds up the algorithms for regions selection and watermark design. The watermark detection is performed within the space/spatial-frequency domain. The detection performance is increased due to the high information redundancy in that domain in comparison with the space or frequency domains, respectively. The performance of the proposed procedure has been tested experimentally for different watermark strengths, i.e., for different values of the peak signal-to-noise ratio (PSNR). The proposed approach provides high detection performance even for high PSNR values. It offers a good compromise between detection performance (including the robustness to a wide variety of common attacks) and imperceptibility.
Xu, M., He, S., Peng, Y., Jin, J., Luo, S., Chia, L.T. & Hu, Y. 2012, 'Content on demand video adaptation based on MPEG-21 digital item adaptation', EURASIP Journal on Wireless Communications and Networking, vol. 2012, no. 104, pp. 1-16.
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One of the major objectives in multimedia research is to provide pervasive access and personalized use of multimedia information. Pervasive access of video data implies the access of cognitive and affective aspects of video content. Personalized use requires the services satisfy individual users needs on video content. This article attempts to provide a content-on-demand (CoD) video adaptation solution by considering users preference on cognitive content and affective content for video media in general, sports video and movies in particular. In this article, CoD video adaptation system is developed to support users decision in selecting their content of interest and adaptively deliver video source by selecting relevant content and dropping frames while considering network conditions. First, video contents are annotated by the description schemes (DSs) provided by MPEG-7 multimedia description schemes (MDSs). Then, to achieve a generic adaptation solution, the adaptation is developed following MPEG-21 Digital Item Adaptation (DIA) framework. We study the MPEG-21 reference software on XML generation and develop our own system for CoD video adaptation in three steps: (1) the content information is parsed from MPEG-7 annotation XML file together with bitstream to generate generic Bitstream Syntax Description (gBSD); (2) Usersâ preference, network characteristic and adaptation QoS (AQoS) are considered for making adaptation decision; (3) adaptation engine automatically parses adaptation decisions and gBSD to achieve adaptation. Unlike most existing adaptation work, the system adapts the content of interest in the video stream according to usersâ preference. We implement the above-mentioned MPEG-7 and MPEG-21 standards and provide a generic video adaptation solution. Adaptation based on gBSD avoids complex video computation. Thirty students from various departments were invited to assess the system and their responses have been positive.
Chen, X., Yang, J., Wu, Q., Zhao, J. & He, S. 2012, 'Directional high-pass filter for blurry image analysis', Signal Processing: Image Communication, vol. 27, no. 7, pp. 760-771.
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High-frequency energy distributions are important characteristics of blurry images. In this paper, directional high-pass filters are proposed to analyze blurry images. Firstly, we show that the proposed directional high-pass filters can effectively estimate the motion direction of motion blurred images. A closed-form solution for motion direction estimation is derived. It achieves a higher estimation accuracy and is also faster than previous methods. Secondly, the paper suggests two important applications of the directional high-frequency energy analysis. It can be employed to identify out-of-focus blur and motion blur, and to detect motion blurred regions in observed images. Experiments on both synthetic and real blurred images are conducted. Encouraging results demonstrate the efficacy of the proposed methods.
Wong, M., He, S., Nguyen, H.T. & Yeh, W. 2012, 'Mass Classification in Digitized Mammograms Using Texture Features and Artificial Neural Network', Lecture Notes in Computer Science, vol. 7667, pp. 151-158.
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A technique is proposed to classify regions of interests (ROIs) of digitized mammograms into mass and non-mass regions using texture features and artificial neural network (ANN). Fifty ROIs were extracted from the MIAS MiniMammographic Database, with 25 ROIs containing masses and 25 ROIs containing normal breast tissue only. Twelve texture features were derived from the gray level co-occurrence matrix (GLCM) of each region. The sequential forward selection technique was used to select four significant features from the twelve features. These significant features were used in the ANN to classify the ROI into either mass or non-mass region. By using leave-one-out method on the 50 images using the four significant features, classification accuracy of 86% was achieved for ANN. The test result using the four significant features is better than the full set of twelve features. The proposed method is compared with some existing works and promising results are obtained
Zeng, C., Jia, W. & He, S. 2012, 'An Algorithm for Colour-based Natural Scene Text Segmentation', Lecture Notes in Computer Science, vol. 7139, pp. 58-68.
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Before the step for text recognition, a text image needs to be segmented into foreground containing only the text area and background. In this paper, a method is proposed for segmenting colour natural scene texts which suffer from a wide range of degradations with complex background. A text image is firstly processed by two 3-means clustering operations with different distance measurements. Then, a modified connected component (CC)-based validation method is used to obtain the text area after clustering. Thirdly, a proposed objective segmentation evaluation method is utilised to choose the final segmentation result from the two segmented text images. The proposed method is compared with other existing methods based on the ICDAR2003 public database. Experimental results show the effectiveness of the proposed method.
Xu, M., Wang, J., He, X., Jin, J.S., Luo, S. & Lu, H. 2012, 'A three-level framework for affective content analysis and its case studies', Multimedia Tools And Applications, vol. 65, pp. 1-23.
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Emotional factors directly reflect audiences attention, evaluation and memory. Recently, video affective content analysis attracts more and more research efforts. Most of the existing methods map low-level affective features directly to emotions by applying machine learning. Compared to human perception process, there is actually a gap between low-level features and high-level human perception of emotion. In order to bridge the gap, we propose a three-level affective content analysis framework by introducing mid-level representation to indicate dialog, audio emotional events (e.g., horror sounds and laughters) and textual concepts (e.g., informative keywords). Mid-level representation is obtained from machine learning on low-level features and used to infer high-level affective content. We further apply the proposed framework and focus on a number of case studies. Audio emotional
Jiang, Y., He, S., Lin, F. & Jia, W. 2011, 'An Encoding and Labeling Scheme Based on Continued Fraction for Dynamic XML', Journal of Software, vol. 6, no. 10, pp. 2043-2049.
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Much research about labeling schemes has been conducted to efficiently determine the ancestor-descendant relationships and the document-order between any two random XML nodes without re-labeling for updates. In this paper, we present an efficient XML encoding and labeling scheme for dynamic XML document, named Continued Fraction-based Encoding (CFE). The proposed CFE scheme labels nodes with continued fractions and has the following three important properties: (1) CFE codes can be inserted between any two consecutive CFE codes with the orders kept and without re-encoding the existing nodes; (2) CFE is orthogonal to specific labeling schemes, thus it can be applied broadly to different labeling schemes or other applications to efficiently process the updates; (3) CFE supports all structural relationships query in XPath. Two test data sets were built for evaluation. The experimental results show that CFE provides fairly reasonable XML query processing performance while completely avoiding relabeling for updates.
Zheng, L., He, S., Wu, Q. & Samali, B. 2011, 'A system for licence plate recognition using a hierarchically combined classifier', International Journal of Intelligent Systems Technologies and Applications, vol. 10, pp. 189-202.
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In a real time, automatic licence plate recognition system, licence detection, character segmentation and character recognition are three important components. All these three components generally require high accuracy and fast recognition speed to process. In this paper, general processing steps for license plate recognition (LPR) are addressed. After three types of combined classifiers are introduced and compared, a hierarchically combined classifier is designed based on an inductive learning-based method and an support vector machine (SVM)-based classification. This approach employs the inductive learning-based method to roughly divide all classes into smaller groups. Then, the SVM approach is used for character classification in individual groups. Having obtained a collection of samples of characters in advance from licence plates after licence detection and character segmentation steps, some known samples are available for training. After the training process, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for next fast training and testing processes based on SVMs. The experimental results show that the hierarchically combined classifier is better than either the inductive learning-based classification or the SVM-based classification with a lower error rate and a faster processing speed.
Du, C., Yang, J., Wu, Q. & He, S. 2011, 'Locating facial landmarks by support vector machine-based active shape model', International Journal of Intelligent Systems Technologies and Applications, vol. 10, pp. 151-170.
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Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence.
Jamdagni, A., Tan, T., Nanda, P., He, S. & Liu, R. 2011, 'Mahalanobis Distance Map Approach for Anomaly Detection of Web-Based Attacks', Journal of Network Forensics, vol. 2, no. 2, pp. 25-39.
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Web serverss and web-based applications are commonly used attack targets. The main issue are how to prevent unauthorized access and to protect web server from the attack. Intrusion Detection Systems and networks. This paper focuses on the detection of various web-based attacks using Geometrical Structure Anomaly Detectin (GSAD) model. Further, a novel algorithm is proposed using Linear Discriminant Analysis (LDA) for the selection of most discriminating features to reduce the computational complexity of payload-based GSAD model. GSAD model is based on a pattern recognition technique used in image payload features to calculate the difference between normal and abnormal network traffice. GSAD model is evaluated experimentally on the real attacks (GATECH) dataset and on the DARPA 1999 dataset.
Yeh, W. & He, S. 2010, 'A New Universal Generating Function Method for Estimating the Novel Multiresource Multistate Information Network Reliability', IEEE Transactions on Reliability, vol. 59, no. 3, pp. 528-538.
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In this article, we introduce a special novel multistate network that permits multiresource to be transmitted from the source node to multiple targets simultaneously without satisfying the flow conservation law. This network is called the multiresource multistate information network (MMIN). The one-to-many-targets (i.e. one-to-all-target-subset) reliability problem of the MMIN is considered next under limited cost and capacity constraint. A straightforward, exact algorithm derived from the universal generating function method (UGFM) is developed for this new problem. The correctness and computational complexity of the proposed UGFM will be analysed and proven. One example is given to illustrate how MMIN reliability is evaluated using the proposed UGFM.
Chen, X., Chung, Y.Y., Bae, C., He, S. & Yeh, W. 2010, 'An Efficient Error Concealment Algorithm for H.264/AVC Using Regression Modelling-Based Prediction', IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2694-2701.
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This paper presents a novel error concealment algorithm for H.264/AVC based on a regression model, which is constructed according to the spatial relationship between block locations and their motion activities. With the proposed algorithm, a corrupted macroblock is partitioned into subblocks and the motion vector of each sub-block is predicted through the regression model with the help of the neighbour motion vectors. The experimental result show that the proposed algorithm can achieve significant Peak Signal Noise Ration (PSNR) improvement over existing methods with even reduced complexity. The implementation of the proposed algorithm is very simple and therefore it can be readily applied to real-time video applications running on various consumer electronics products such as mobile devices
He, S., Wu, Q., Hintz, T.B. & Jia, W. 2008, 'Gradient-Based Edge Detection on a Hexagonal Structure', Business Review, vol. 3, no. 1, pp. 133-143.
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Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.
He, S., Wu, Q., Jia, W. & Hintz, T.B. 2008, 'Edge Detection on Hexagonal Structure', Journal of Algorithms & Computational Technoiogy, vol. 2, no. 1, pp. 61-78.
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Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.
Zheng, L. & He, S. 2008, 'A Comparison of Reduced Support Vector Machines', International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 3/4, pp. 301-312.
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Wang, H., He, S., Hintz, T.B. & Wu, Q. 2008, 'Fractal Image Compression on Hexagonal Structure', Journal of Algorithms & Computational Technoiogy, vol. 2, no. 1, pp. 79-97.
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Fractal image compression (FIC) is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel hexagonal image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. In this paper, two presentations of SA on the traditional display device will be discussed. Then we will review the current research work on fractal image compression based on SA using both presentations. The FIC performance on SA will be compared with it on the traditional square structure in terms of compression ratio and PSNR. In the experimental results, higher PSNR values can be achieved at various compression ratios for all test images. The preliminary research on this direction has shown a promising future of applying FIC on SA to further improve the compression performance.
Zheng, L. & He, X. 2008, 'A comparison of reduced support vector machines', International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 3-4, pp. 301-312.
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In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). This includes not only SVM's dual forms and solution forms but also the general process and properties of SVM. Then aiming at speeding up SVMs, some kinds of Reduced SVMs (RSVMs) are discussed in detailed. A comparison among them is presented in several aspects. Finally, we show research issues that need to be resolved or investigated further. A number of future trends are also briefly sketched. © 2008 Inderscience Enterprises Ltd.
Jia, W., Zhang, H. & He, S. 2007, 'Region-based License Plate Detection', Journal Of Network And Computer Applications, vol. 30, no. 4, pp. 1324-1333.
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Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods.
He, S. 2007, 'Editorial - Special Issue On Information Technology', Journal Of Network And Computer Applications, vol. 30, no. 4, pp. 1273-1274.
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Zhang, H., Jia, W., He, S. & Wu, Q. 2007, 'Learning-Based License Plate Detection in Vehicle Image Database', International Journal of Intelligent Information and Database Systems, vol. 1, no. 2, pp. 228-243.
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This paper proposes a learning-based algorithm to detect license plates of vehicles from vehicle image database. There are three main contributions in this paper. The first contribution is to define a novel vertical edge map, which makes the image processing more effectively. The second contribution is to propose a learning-based cascade classifier composing of two kinds of sub-classifiers, which makes the system very robust. The third contribution is to experimentally estimate the parameter of scaling factor and chose an optimal one for the algorithm to seek a good balance between detection rate and processing time.
He, S. & Lu, Z. 2007, 'Editorial - Special Issue On Information Technology', International Journal on Agent-Oriented Software Engineering, vol. 1, no. 2, pp. 145-146.
He, S., Wang, H., Jia, W., Wu, Q., Hur, N., Kim, J. & Hintz, T.B. 2007, 'Uniform Image Partitioning for Fractal Compression on Virtual Hexagonal Structure', International Journal of Information and Systems Science, vol. 3, no. 3, pp. 492-509.
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Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexag-onal structure can be described in terms of a hexagonal grid. Uniformly sepa-rating image into seven similar copies with a smaller scale has commonly been used for parallel and accurate image processing including image compression on hexagonal structure. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. Furthermore, the current techniques used for uniform separation of images on hexagonal structure do not coincide with the rectangular shape of images. This has been an obstacle in the use of hexagonal structure for image processing. In this paper, we briefly review a newly developed virtual hexagonal structure that is scalable. Based on this virtual structure, algorithms for uni-form image separation are presented. The virtual hexagonal structure retains image resolution during the process of image separation, and does not intro-duce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure while the image shape is kept in rectangle. As an application of image partitioning, we present a Fractal Image Compression (FIC) method on the virtual image struc- ture by adopting Fisher's basic FIC method on the traditional square image structure. The modifcation on the definition of range block and domain block is implemented in order to utilize the enhanced image structure. The results of the FIC approach applied to testing images are analyzed and show higher fidelity.
He, X. 2007, 'Editorial', Journal of Network and Computer Applications, vol. 30, no. 4, pp. 1273-1274.
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Wu, Q., He, S., Hintz, T.B. & Ye, Y. 2006, 'A novel and uniform image partitioning on spiral architecture', International journal of computational science and engineering, vol. 2, no. 1, pp. 57-63.
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He, S., Jia, W., Wu, Q. & Hintz, T.B. 2006, 'Description of the cardiac movement using hexagonal image structures', Computerised medical imaging and graphics, vol. 30, no. 6-7, pp. 377-382.
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The most notable characteristic of the heart is its movement. Detection of dynamic information describing cardiac movement such as amplitude, speed and acceleration facilitates interpretation of normal and abnormal function. In recent years, the Omni-directional M-mode Echocardiography System (OMES) has been developed as a process that builds moving information from a sequence of echocardiography image frames. OMES detects cardiac movement through construction and analysis of PositionTime Grey Waveform (PTGW) images on some feature points of the boundaries of the ventricles. Image edge detection plays an important role in determining the feature boundary points and their moving directions as the basis for extraction of PTGW imagesSpiral Architecture (SA) has proved efficient for image edge detection. SA is a hexagonal image structure in which an image is represented as a collection of hexagonal pixels. There are two operations called spiral addition and spiral multiplication defined on SA. They correspond to image translation and rotation, respectively. In this paper, we perform ventricle boundary detection based on SA using various defined chain codes. The gradient direction of each boundary point is determined at the same time. PTGW images at each boundary point are obtained through a series of spiral additions according to the directions of boundary points. Unlike the OMES system, our new approach is no longer affected by the translation movement of the heart. As its result, three curves representing the amplitude, speed and acceleration of cardiac movement can be easily drawn from the PTGW images obtained. Our approach is more efficient and accurate than OMES, and our results contain a more robust and complete description of cardiac motion.
He, S. & Arabnia, H. 2006, 'Design of a Uni-Directional multi ring switch', International Journal of Computer Science and Network Security, vol. 6, pp. 130-138.
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Zhang, H., He, S. & Wu, Q. 2006, 'Generic Object Detection', Journal of Yunnan Nationalities University, vol. 15, no. 4, pp. 261-267.
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Wu, Q., He, X., Hintz, T. & Ye, Y. 2006, 'A novel and uniform image partitioning on spiral architecture', International Journal of Computational Science and Engineering, vol. 2, no. 1-2, pp. 57-63.
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Uniform image partitioning based on spiral architecture plays an important role in parallel image processing in many aspects such as uniform data partitioning, load balancing, zero data exchange between the processing nodes et al. However, when the number of partitions is not the power of seven like 7, 49, every sub-image except one is split into a few fragments which are mixed together. We could not tell which fragments belong to which subimage. It is an unacceptable flaw to parallel image processing. This paper proposes a method to resolve the problem mentioned above. From the experimental results, it is shown that the proposed method correctly identifies the fragments belonging to the same subimage and successfully collects them together to be a complete subimage. Then, these subimages can be distributed into the different processing nodes for further processing. Copyright © 2006, Inderscience Publishers.
Liu, D. & He, S. 2006, 'Novel block matching compression algorithm for video images', Xitong Fangzhen Xuebao / Journal of System Simulation, vol. 18, no. 1, pp. 47-49.
At present, video image compression is the hot topic of image compression and coding. Many useful results of bivariable functions have been obtained by studying the bivariable fractal space. A block matching intra-frame video image compression algorithm based on bivariable fractal function space theory has been proposed. The framework of the algorithm and an effective cost function of inter-frame coding were given. A novel block matching compression algorithm for video images was formed. The reliable result is available.
Wang, H., Wang, M., Hintz, T.B., Wu, Q. & He, S. 2005, 'VSA-based Fractual Image Compression', Journal WSCG, vol. 13, no. 1-3, pp. 89-96.
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Liu, D., He, S., Ma, Y. & Jin, Y. 2003, 'A Kind of Video Image Compression Algorithm Based on Bivariable Fractal Function', Journal of System Simulation, vol. 15, pp. 72-76.
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Wu, Q., He, S. & Hintz, T.B. 2002, 'Image Rotation Without Scaling on Spiral Architecture', Journal of WSCG, vol. 10, no. 2, pp. 515-520.
Image Rotation Without Scaling on Spiral Architecture
He, S., Hintz, T.B. & Szewcow, U. 1998, 'Replicated Shared Object Model For Edge Detection With Spiral Architecture', Parallel And Distributed Processing, vol. 1388, pp. 252-260.
Edge detection in computer vision and image processing is a process which detects one kind of significant features appearing as discontinuities in intensities. A parallel edge detection algorithm based on Spiral Architeture is designed in this paper by a
He, S., Hintz, T.B. & Szewcow, U. 1998, 'Replicated Shared Object Model For Parallel Edge Detection Algorithm Based On Spiral Architecture', Future Generation Computer Systems, vol. 14, no. 5-6, pp. 341-350.
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Edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as discontinuities in intensities. A parallel edge detection algorithm based on spiral architecture is designed in
He, X. 1996, 'Oscillations of interconnected systems with C0 nonlinearities', Journal of the Australian Mathematical Society Series B-Applied Mathematics, vol. 38, no. 1, pp. 41-62.
In this paper we establish conditions which ensure the existence of self-excited oscillations in complex dynamical systems with nondifferentiable nonlinearities, by considering those types of systems which can be viewed as an interconnection of several simpler subsystems. We find that the nonlinear terms of the system in which we are interested do not need to satisfy the Lipschitz condition. © Australian Mathematical Society, 1996.