Yao, L, Kusakunniran, W, Wu, Q, Zhang, J, Tang, Z & Yang, W 2019, 'Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image', Pattern Recognition Letters.View/Download from: UTS OPUS or Publisher's site
© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.
Siriapisith, T, Kusakunniran, W & Haddawy, P 2019, '3D segmentation of exterior wall surface of abdominal aortic aneurysm from CT images using variable neighborhood search.', Computers in Biology and Medicine, vol. 107, pp. 73-85.View/Download from: UTS OPUS or Publisher's site
A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.
Kusakunniran, W, Wu, Q, Ritthipravat, P & Zhang, J 2018, 'Hard exudates segmentation based on learned initial seeds and iterative graph cut', COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol. 158, pp. 173-183.View/Download from: UTS OPUS or Publisher's site
Kusakunniran, W, Dahal, AS & Viriyasitavat, W 2018, 'Journal Co-Citation Analysis for Identifying Trends of Inter-Disciplinary Research: An Exploratory Case Study in a University', Journal of Information and Knowledge Management, vol. 17, no. 4.View/Download from: UTS OPUS or Publisher's site
© 2018 World Scientific Publishing Co. A journal stands as a marker of the intellectual space which holds vast areas of literature. Exploring and analysing the underlined knowledge sources and themes of the journal literature will significantly benefit any institution to identify the key intellectual domain strength and improve in research collaboration. The main objective of this paper is to identify inter-disciplinary trends of research for one university as a case study, using journal co-citation analysis. With the help of correlation metric and cluster analysis, the published literature between 2004 and 2013 from seven subject domains (i.e. including medicine, pharmacy and pharmacology, biological sciences, linguistics, modern languages, chemistry, and computer science and information systems) are analysed and interpreted. The results can demonstrate that there are the strong research dominance in the medical field and the prospective collaboration in social science and chemistry. In addition, the interpretation of the findings could be served as a foundation for future research in the direction of strong bonding between inter-disciplinary fields.
Siriapisith, T, Kusakunniran, W & Haddawy, P 2018, 'Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces.', Journal of Digital Imaging, vol. 31, no. 4, pp. 490-504.View/Download from: UTS OPUS or Publisher's site
Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.
Kusakunniran, W, Wu, Q, Li, H, Zhang, J & Wang, L 2014, 'Recognizing Gaits across Views through Correlated Motion Co-clustering', IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 696-709.View/Download from: UTS OPUS or Publisher's site
Kusakunniran, W 2014, 'Attribute-based learning for gait recognition using spatio-temporal interest points', IMAGE AND VISION COMPUTING, vol. 32, no. 12, pp. 1117-1126.View/Download from: Publisher's site
Zhang, J, Wu, Q, Kusakunniran, W, Ma, Y & Li, H 2013, 'A New View-Invariant Feature for Cross-View Gait Recognition', IEEE Transactions on Information Forensics and Security, vol. 8, no. 10, pp. 1642-1653.View/Download from: UTS OPUS or Publisher's site
Human gait is an important biometric feature which is able to identify a person remotely. However, change of view causes significant difficulties for recognizing gaits. This paper proposes a new framework to construct a new view-invariant feature for cross-view gait recognition. Our view-normalization process is performed in the input layer (i.e., on gait silhouettes) to normalize gaits from arbitrary views. That is, each sequence of gait silhouettes recorded from a certain view is transformed onto the common canonical view by using corresponding domain transformation obtained through invariant low-rank textures (TILTs). Then, an improved scheme of procrustes shape analysis (PSA) is proposed and applied on a sequence of the normalized gait silhouettes to extract a novel view-invariant gait feature based on procrustes mean shape (PMS) and consecutively measure a gait similarity based on procrustes distance (PD). Comprehensive experiments were carried out on widely adopted gait databases. It has been shown that the performance of the proposed method is promising when compared with other existing methods in the literature.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2012, 'Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron', Pattern Recognition Letters, vol. 33, pp. 882-889.View/Download from: UTS OPUS or Publisher's site
Gait has been shown to be an efficient biometric feature for human identification at a distance. However, performance of gait recognition can be affected by view variation. This leads to a consequent difficulty of cross-view gait recognition. A novel method is proposed to solve the above difficulty by using view transformation model (VTM). VTM is constructed based on regression processes by adopting multi-layer perceptron (MLP) as a regression tool. VTM estimates gait feature from one view using a well selected region of interest (ROI) on gait feature from another view. Thus, trained VTMs can normalize gait features from across views into the same view before gait similarity is measured. Moreover, this paper proposes a new multi-view gait recognition which estimates gait feature on one view using selected gait features from several other views. Extensive experimental results demonstrate that the proposed method significantly outperforms other baseline methods in literature for both cross-view and multi-view gait recognitions. In our experiments, particularly, average accuracies of 99%, 98% and 93% are achieved for multiple views gait recognition by using 5 cameras, 4 cameras and 3 cameras respectively.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2012, 'Gait Recognition across Various Walking Speeds using Higher-order Shape Configuration based on Differential Composition Model', IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 6, pp. 1654-1668.View/Download from: UTS OPUS or Publisher's site
Gait has been known as an effective biometric feature to identify a person at a distance. However, variation of walking speeds may lead to significant changes to human walking patterns. It causes many difficulties for gait recognition. A comprehensive analysis has been carried out in this paper to identify such effects. Based on the analysis, Procrustes shape analysis is adopted for gait signature description and relevant similarity measurement. To tackle the challenges raised by speed change, this paper proposes a higher order shape configuration for gait shape description, which deliberately conserves discriminative information in the gait signatures and is still able to tolerate the varying walking speed. Instead of simply measuring the similarity between two gaits by treating them as two unified objects, a differential composition model (DCM) is constructed. The DCM differentiates the different effects caused by walking speed changes on various human body parts. In the meantime, it also balances well the different discriminabilities of each body part on the overall gait similarity measurements. In this model, the Fisher discriminant ratio is adopted to calculate weights for each body part. Comprehensive experiments based on widely adopted gait databases demonstrate that our proposed method is efficient for cross-speed gait recognition and outperforms other state-of-the-art methods.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2012, 'Gait Recognition Under Various Viewing Angles Based On Correlated Motion Regression', Ieee Transactions On Circuits And Systems For Video Technology, vol. 22, no. 6, pp. 966-980.View/Download from: UTS OPUS or Publisher's site
It is well recognized that gait is an important biometric feature to identify a person at a distance, e. g., in video surveillance application. However, in reality, change of viewing angle causes significant challenge for gait recognition. A novel approa
Yao, L, Kusakunniran, W, Wu, Q, Zhang, J & Tang, Z 2018, 'Robust CNN-based Gait Verification and Identification using Skeleton Gait Energy Image', 2018 Digital Image Computing: Techniques and Applications (DICTA), Digital Image Computing: Techniques and Applications, IEEE, Canberra, Australia.View/Download from: UTS OPUS or Publisher's site
Kasantikul, R & Kusakunniran, W 2018, 'Improving Supervised Microaneurysm Segmentation using Autoencoder-Regularized Neural Network', 2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), International Conference on Digital Image Computing - Techniques and Applications (DICTA), IEEE, Canberra, AUSTRALIA, pp. 553-559.View/Download from: UTS OPUS or Publisher's site
This paper proposes the novel microaneurysm segmentation technique, based on the autoencoder-regularized neural network model. The proposed method is developed using two levels of the segmentation. First, the coarse-level segmentation stage locates the candidate areas using the multi-scale correlation filter and region growing. Second, the fine-level segmentation stage uses the neural network to obtain confidence values of candidate areas of being microaneurysm. The neural network based technique introduced in this paper is the modified multilayer neural network with an additional branch to take into account of the reconstruction error (in a similar fashion to the autoencoder). This modification to the neural network results in the consistent improvement in the classification performance, when compared to the conventional network without such modification. The proposed method is evaluated using the retinopathic online challenge dataset. It can deliver very promising results, when compared with the existing state-of-the-art techniques.
Siriapisith, T, Kusakunniran, W & Haddawy, P 2018, 'A General Approach to Segmentation in CT Grayscale Images using Variable Neighborhood Search', 2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), International Conference on Digital Image Computing - Techniques and Applications (DICTA), IEEE, Canberra, AUSTRALIA, pp. 447-453.View/Download from: UTS OPUS
Tirasirichai, B, Thanomboon, P, Soontorntham, P, Kusakunniran, W & Robinson, M 2018, 'Bloom Balance: Calorie Balancing Application with Scientific Validation', 2018 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, Mahidol Univ, Fac ICT, THAILAND, pp. 305-310.View/Download from: UTS OPUS or Publisher's site
Kusakunniran, W, Wu, Q & Zhang, J 2017, 'Action Recognition based on Correlated Codewords of Body Movements', Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), International Conference on Digital Image Computing: Techniques and Applications, IEEE, Sydney, Australia, pp. 1-8.View/Download from: UTS OPUS or Publisher's site
Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest points are detected in each action video. Then, feature descriptors are extracted from these key points and clustered into different codewords implicitly representing different characteristics of motions. The final representation of each action video is a combination of a bag of words and correlations between codewords. Then, the support vector machine is used as a classification tool. Based on the experimental results, the proposed method achieves a very promising performance and particularly outperforms the other existing methods that rely on spatio-temporal features.
Kusakunniran, W, Wul, Q, Ritthipravad, P & Zhang, J 2017, 'Three-stages hard exudates segmentation in retinal images', 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017, 2017 9th International Conference on Information Technology and Electrical Engineering, IEEE, Phuket, Thailand, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. This paper proposes a three-stages method of hard exudate segmentation in retinal images. The first stage is the pre-processing. The color transfer is applied to make all retinal images to have the same color characteristics, based on statistical analysis. Then, only a yellow channel of each image is used in the further analysis. The second stage is the blob initialization. The blob detection based on color, size, and shape including circularity and convexity is used to identify initial pixels of hard exudates. The detected blobs must not be inside the optic disk. The third stage is the segmentation. The graph cut is iteratively applied on partitions of the image. The fine-tune segmentation in sub-images is necessary because the portion of hard exudates is significantly less than the portion of non-hard exudates. The proposed method is evaluated using the two well-known datasets, namely e-ophtha and DIARETDB1, in both aspects of pixel-level and image-level. Based on the comprehensive comparisons with the existing works, the proposed method is shown to be very promising. In the image-level, it achieves 96% sensitivity and 94% specificity for the e-ophtha dataset, and 96% sensitivity and 98% specificity for the DIARETDB1 dataset.
Yao, L, Kusakunniran, W, Wu, Q, Zhang, J & Tang, Z 2017, 'Robust Gait Recognition under Unconstrained Environments using Hybrid Descriptions', Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), International Conference on Digital Image Computing: Techniques and Applications, IEEE, Sydney, Australia.View/Download from: UTS OPUS or Publisher's site
Gait is one of the key biometric features that has been widely applied for human identification. Appearance-based features and motion-based features are the two mainly used presentations in the gait recognition. However, appearance-based features are sensitive to the body shape changes and silhouette extraction from real-world images and videos also remains a challenge. As for motion features, due to the difficulty in extracting the underlying models from gait sequences, the localization of human joints lacks of high reliability and strong robustness. This paper proposes a new approach which utilizes Two-Point Gait (TPG) as the motion feature to remedy the deficiency of the appearance feature based on Gait Energy Image (GEI), in order to increase the robustness of gait recognition under the unconstrained environments with view changes and cloth changes. Another contribution of this paper is that this is the first time that TPG has been applied for view change and cloth change issues since it was proposed. The extensive experiments show that the proposed method is more invariant to the view change and cloth change, and can significantly improve the robustness of gait recognition.
Jiang, C, Kusakunniran, W, Pornprasatpol, N, Limsuwankesorn, C & Li, Y 2017, 'Smart Security Guard Scheduling System Based On the Reinforcement Learning', 2017 21ST INTERNATIONAL COMPUTER SCIENCE AND ENGINEERING CONFERENCE (ICSEC 2017), 21st International Computer Science and Engineering Conference (ICSEC), IEEE, King Mongkuts Inst Technol Ladkrabang, Fac Engn, Dept Comp Engn, Bangkok, THAILAND, pp. 214-218.
Kanchanapreechakorn, S & Kusakunniran, W 2017, 'Robust Human Re-identification using Mean Shape Analysis of Face Images', TENCON 2017 - 2017 IEEE REGION 10 CONFERENCE, IEEE Region 10 Conference (TENCON), IEEE, MALAYSIA, pp. 901-905.
Kristiadi, DP, Udjaja, Y, Supangat, B, Prameswara, RY, Warnars, HLHS, Heryadi, Y & Kusakunniran, W 2017, 'THE EFFECT OF UI,UX and GX ON VIDEO GAMES', 2017 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS AND COMPUTATIONAL INTELLIGENCE (CYBERNETICSCOM), IEEE International Conference on Cybernetics and Computational Intelligence (IEEE CyberneticsCom), IEEE, Phuket, THAILAND, pp. 158-163.
Kusakunniran, W, Prachasri, N, Dirakbussarakom, N & Yangchaem, D 2017, 'Distinguishing ACL Patients from Healthy Individuals using Multilayer Perceptron on Motion Patterns', 2017 9TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SMART TECHNOLOGY (KST), 9th International Conference on Knowledge and Smart Technology (KST), IEEE, Pattaya, THAILAND, pp. 1-5.
Yoopoo, K, Ongsritakul, S, Tirasirichai, B, Kusakunniran, W & Robinson, M 2017, 'Regression Model for Predicting the Maximum Load of the Movement', 2017 2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY (INCIT), 2nd International Conference on Information Technology (INCIT), IEEE, Nakhonpathom, THAILAND, pp. 76-79.
Krungkaew, R & Kusakunniran, W 2016, 'Foreground Segmentation in a Video by using a Novel Dynamic Codebook', Proceedings of the 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, Chiang Mai, THAILAND, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
Foreground segmentation in a video is a key process in many applications such as object detection, an object tracking, and a behavior analysis. Since the extracted foreground objects are often used in the analytical process, the quality of the foreground is a significant factor to the success of these applications. However, there are many key challenges in the foreground segmentation, including dynamic backgrounds, gradual illumination changes, sudden illumination changes, shadows, and long-term scene changes. This paper proposes a novel dynamic codebook method to address such challenges. The dynamic codebook aims to significantly improve the conventional well-known codebook technique by introducing a technique to make a dynamic boundary of each codeword. In this technique, the lab color space is used in order to make the model more resilient to the illumination change. The experimental results and comprehensive comparisons demonstrate that the proposed method can achieve very promising performance.
Kusakunniran, W, Rattanachoosin, J, Sutassananon, K & Anekkitphanich, P 2016, 'Automatic Quality Assessment and Segmentation of Diabetic Retinopathy Images', Proceedings of the 2016 IEEE Region 10 Conference (TENCON), IEEE Region 10 Conference (TENCON), IEEE, Singapore, pp. 997-1000.View/Download from: UTS OPUS or Publisher's site
Diabetes, often referred to as diabetes mellitus, describes a group of metabolic diseases whose patients are diagnosed with having a high glucose level in blood. One form of diabetes commonly found in Thailand is Diabetic Retinopathy, in which prevalent symptoms of diabetes are very likely to induce vision impairment or even blindness.
Thus, the ability to detect early symptoms of diabetes through retinal images is proven vital for proper medical treatments to reduce the risk of the loss of sight. Although recent researches have proposed several methodologies for assessing the severity level according to the condition of retinal components from retinal images, issues over the accuracy and precision of the evaluation model compel the improvement for a better model to be practically implemented with more favorable outcome. Earlier researches have introduced several methods such as the image quality assessment based on the histogram back projection , or the image segmentation for the diagnosis of the diabetic retinopathy severity level via the construction of Echo State Neural Network (ESNN).
This paper proposes methods to assess the image quality and to segment the image components for analyzing the disease severity level of a retinal image. Four major information constituting the image quality, namely color, contrast, focus, and illumination, are extracted in order to evaluate the overall image gradability. By classifying images according to their image quality factors, the Principal Component Analysis (PCA) technique is applied to reduce the data dimensionality and vividly project discriminant features over minor misleading details that could hamper the accuracy of the evaluation result. Then, the k-nearest neighbor is applied as a classification tool.
Moreover, regarding the identification of components such as hard exudates in a retinal image  , image segmentation techniques are applied in order to deduce the representation of each retinal component...
Prachasri, N, Yangchaem, D, Dirakbussarakom, N & Kusakunniran, W 2016, 'Differentiation of Motion Patterns between Anterior Cruciate Ligament Injuries and Healthy Individuals', Proceedings of the 5th 2016 ICT International Student Project Conference (ICT-ISPC), 5th ICT International Student Project Conference (ICT-ISPC), IEEE, Nakhon Pathom, Thailand, pp. 109-112.View/Download from: UTS OPUS or Publisher's site
Detecting the sign of Anterior Cruciate Ligament (ACL) injury in advance is very important because the injury could be prevented to seriously occur, or a patient would be provided with the proper treatment timely. ACL injury, mostly happen with athletes, is the injury that one pair of cruciate ligament are torn because of the sport or daily life activities. Finding the differences of gait data patterns between an ACL injury patient and a healthy controlled person is proposed in this paper. Linear Discriminant Analysis (LDA) is applied to assist in the discrimination. After that, the k-nearest neighbor algorithm which focuses on the subject average is applied as a classification tool. The leave-one-out cross-validation is used for evaluating the performance of the proposed method. In this paper, the friendly user interface is also developed to facilitate the general users for using this program. In the experiment, there are 10 subjects which consist of 5 healthy control subjects and other 5 ACL injury subjects. The 3 gait variables are used to verify the proposed method. The 3 variables are the top 3 most significant variables which are ankle joint moment, hip joint moment and knee joint moment. In the experiment, it is shown that the proposed method can achieve a promising accuracy with the limited number of training data.
Worrawichaipat, P, Bhakkalin, S, Suthisa-ngiam, T & Kusakunniran, W 2016, 'I'm Road, Fury Traffic Car Running Game Application', 2016 5th ICT International Student Project Conference (ICT-ISPC), 5th ICT International Student Project Conference (ICT-ISPC), IEEE, Nakhon Pathom, Thailand, pp. 174-177.View/Download from: UTS OPUS or Publisher's site
This paper describes the project, called 'I'm Road, Fury Traffic', that aims to develop the game application for entertainment which also evokes conscious mind of good practices in driving car. It is a casual game that the player will be present as a car driver who has to reach the destination in a specific time and also have to follow the driving rules. The game scenes have simulated the real situation from the road in Thailand. They also consist of some obstacles such as road constructions, road-side shops, and parking cars. In addition, the finite state machine is used to make different cars in game be able to think by themselves and to provide the different behaviors. Moreover, there is the mission system to set the aims for the player and the shop systems where the player can spend money in the game to upgrade the performance of the game play. The player will win when reaching the destination or the game will be terminated if the player hits other cars or obstacles for three times. At the end of the game, the player will get rewards (i.e. money in the game) for good behaviors of car driving during the game play. The proposed application has been evaluated mainly in three aspects including entertainment, awareness of good practices in driving car, and challenging missions.
Kusakunniran, W 2015, 'Extracting Gait Figures in a Video based on Markerless Motion', 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Seventh International Conference on Knowledge and Systems Engineering (KSE), IEEE, Ho Chi Minh City, VIETNAM, pp. 306-309.View/Download from: Publisher's site
Kusakunniran, W, Dirakbussarakom, N, Prachasri, N, Yangchaem, D, Vanrenterghem, J & Robinson, M 2015, 'Discriminating motion patterns of ACL reconstructed patients from healthy individuals', 2015 14TH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA), 14th IAPR International Conference on Machine Vision Applications (MVA), IEEE, Tokyo, JAPAN, pp. 447-450.
Sarutiyapithorn, N, Wisawanart, N, Saowaneepitak, M, Kusakunniran, W & Thongkanchorn, K 2015, 'Electronic Health Information Standard based on CDA for Thai Medical System: focused on Medical Procedures in Medium-sized Hospitals (HOSxP)', PROCEEDINGS OF THE 2015 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE), 12th int joint conf comp sci software engn (Jcsse), IEEE, prince songkla univ, hat yai, THAILAND, pp. 97-101.
Wangkrapong, P, Suvanprateeb, T, Kusolnumpa, S, Kusakunniran, W & Thongkanchorn, K 2015, 'Electronic Health Information Standard for Patient Transfer based on CDA, focused on Patient Information in Small-sized Hospitals (JHCIS)', 2015 INTERNATIONAL COMPUTER SCIENCE AND ENGINEERING CONFERENCE (ICSEC), International Computer Science and Engineering Conference (ICSEC), IEEE, Chiang Mai, THAILAND, pp. 33-38.
Chomchalerm, G, Rattanakajornsak, J, Samsrisook, U, Wongsawang, D & Kusakunniran, W 2014, 'Braille Diet Dictionary Application for the Blind on Android Smartphone', 2014 THIRD ICT INTERNATIONAL STUDENT PROJECT CONFERENCE (ICT-ISPC), 3rd ICT International Student Project Conference (lSPC), IEEE, Mahidol Univ, Fac ICT, THAILAND, pp. 143-146.
Kusakunniran, W, satoh, S, Zhang, J & Wu, Q 2013, 'Attribute-based learning for large scale object classification', 2013 IEEE International Conference on Multimedia and Expo, IEEE International Conference on Multimedia and Expo, IEEE, San Jose, California, USA, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
Scalability to large numbers of classes is an important challenge for multi-class classification. It can often be computationally infeasible at test phase when class prediction is performed by using every possible classifier trained for each individual class. This paper proposes an attribute-based learning method to overcome this limitation. First is to define attributes and their associations with object classes automatically and simultaneously. Such associations are learned based on greedy strategy under certain conditions. Second is to learn a classifier for each attribute instead of each class. Then, these trained classifiers are used to predict classes based on their attribute representations. The proposed method also allows trade-off between test-time complexity (which grows linearly with the number of attributes) and accuracy. Experiments based on Animals-with-Attributes and ILSVRC2010 datasets have shown that the performance of our method is promising when compared with the state-of-the-art.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2011, 'Pairwise Shape configuration-based PSA for gait recognition under small viewing angle change', 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on Video and Signal Based Surveillance (AVSS), IEEE, Klagenfurt, Austria, pp. 17-22.View/Download from: UTS OPUS or Publisher's site
Two main components of Procrustes Shape Analysis (PSA) are adopted and adapted specifically to address gait recognition under small viewing angle change: 1) Procrustes Mean Shape (PMS) for gait signature description; 2) Procrustes Distance (PD) for similarity measurement. Pairwise Shape Configuration (PSC) is proposed as a shape descriptor in place of existing Centroid Shape Configuration (CSC) in conventional PSA. PSC can better tolerate shape change caused by viewing angle change than CSC. Small variation of viewing angle makes large impact only on global gait appearance. Without major impact on local spatio-temporal motion, PSC which effectively embeds local shape information can generate robust view-invariant gait feature. To enhance gait recognition performance, a novel boundary re-sampling process is proposed. It provides only necessary re-sampled points to PSC description. In the meantime, it efficiently solves problems of boundary point correspondence, boundary normalization and boundary smoothness. This re-sampling process adopts prior knowledge of body pose structure. Comprehensive experiment is carried out on the CASIA gait database. The proposed method is shown to significantly improve performance of gait recognition under small viewing angle change without additional requirements of supervised learning, known viewing angle and multi-camera system, when compared with other methods in literatures.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2011, 'Speed-invariant gait recognition based on Procrustes Shape Analysis using higher-order shape configuration', 2011 18th IEEE International Conference on Image Processing (ICIP), IEEE International Conference on Image Processing, IEEE, Brussels, Belgium, pp. 545-548.View/Download from: UTS OPUS or Publisher's site
Walking speed change is considered a typical challenge hindering reliable human gait recognition. This paper proposes a novel method to extract speed-invariant gait feature based on Procrustes Shape Analysis (PSA). Two major components of PSA, i.e., Procrustes Mean Shape (PMS) and Procrustes Distance (PD), are adopted and adapted specifically for the purpose of speed-invariant gait recognition. One of our major contributions in this work is that, instead of using conventional Centroid Shape Configuration (CSC) which is not suitable to describe individual gait when body shape changes particularly due to change of walking speed, we propose a new descriptor named Higher-order derivative Shape Configuration (HSC) which can generate robust speed-invariant gait feature. From the first order to the higher order, derivative shape configuration contains gait shape information of different levels. Intuitively, the higher order of derivative is able to describe gait with shape change caused by the larger change of walking speed. Encouraging experimental results show that our proposed method is efficient for speed-invariant gait recognition and evidently outperforms other existing methods in the literatures.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2010, 'Multi-view Gait Recognition Based on Motion Regression using Multilayer Perceptron', Proceedings: 2010 20th International Conference Pattern Recognition (ICPR 2010), International Conference Pattern Recognition, IEEE Computer Society, Istanbul Turkey, pp. 2186-2189.View/Download from: UTS OPUS or Publisher's site
It has been shown that gait is an efficient biometric feature for identifying a person at a distance. However, it is a challenging problem to obtain reliable gait feature when viewing angle changes because the body appearance can be different under the various viewing angles. In this paper, the problem above is formulated as a regression problem where a novel View Transformation Model (VTM) is constructed by adopting Multilayer Perceptron (MLP) as regression tool. It smoothly estimates gait feature under an unknown viewing angle based on motion information in a well selected Region of Interest (ROI) under other existing viewing angles. Thus, this proposal can normalize gait features under various viewing angles into a common viewing angle before gait similarity measurement is carried out. Encouraging experimental results have been obtained based on widely adopted benchmark database.
Kusakunniran, W, Wu, Q, Zhang, J & Li, H 2010, 'Support Vector Regression for Multi-view Gait Recognition Based on Local Motion Feature Selection', 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, San Francisco CA, USA, pp. 974-981.View/Download from: UTS OPUS or Publisher's site
Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve view-independent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature.
Kusakunniran, W, Li, H & Zhang, J 2009, 'A direct method to self-calibrate a surveillance camera by observing a walking pedestrian', 2009 Digital Image Computing: Techniques and Applications, Digital Image Computing Techniques and Applications, IEEE, Melbourne, VIC, pp. 250-255.View/Download from: UTS OPUS or Publisher's site
Recent efforts show that it is possible to calibrate a surveillance camera simply from observing a walking human. This procedure can be seen as a special application of the camera self-calibration technique. Several methods have been proposed along this
Kusakunniran, W, Wu, Q, Li, H & Zhang, J 2009, 'Automatic gait recognition using weighted binary pattern on video', Proceedings of Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Advanced Video and Signal Based Surveillance, IEEE Computer Society, Genoa, Italy, pp. 49-54.View/Download from: UTS OPUS
Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary Pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. Then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. The proposed method is validated on several well known benchmark databases. The extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.
Kusakunniran, W, Wu, Q, Li, H & Zhang, J 2009, 'Multiple Views Gait Recognition using View Transformation Model Based on Optimized Gait Energy Image', Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops, IEEE International Conference on Computer Vision Workshops, IEEE, Kyoto, Japan, pp. 1058-1064.View/Download from: UTS OPUS
Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.