About
Welcome to the Video, Image, and Sound Analysis Lab (VISAL) at the City University of Hong Kong! The lab is directed by Dr. Antoni Chan in the Department of Computer Science.
Our main research activities include:
- Computer Vision, Surveillance
- Machine Learning, Pattern Recognition
- Computer Audition, Music Information Retrieval
- Eye Gaze Analysis
For more information about our current research, please visit the projects and publication pages.
Opportunities for graduate students and research assistants - if you are interested in joining the lab, please check this information.
Latest News [more]
- [Jun 28, 2016]
Congratulations to Sijin for defending his thesis!
- [Jun 25, 2016]
Congratulations to Adeel for winning a “Best Research Paper Award 2013/14″ from the Higher Education Commission (HEC) of Pakistan for his TPAMI 2013 paper!
- [Apr 26, 2016]
Congratulations to Huy for defending his Thesis!
- [Mar 18, 2016]
Congratulations to Zheng for defending his thesis!
Recent Publications [more]
- Scanpath modeling and classification with Hidden Markov Models.
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Behavior Research Methods, to appear. - Color Orchestra: Ordering Color Palettes for Interpolation and Prediction.
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IEEE Transactions on Visualization and Computer Graphics (TVCG), to appear. - Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.
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Vision Research, to appear, 2017. - Video Desnowing and Deraining Based on Matrix Decomposition.
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, Jul 2017. - Martial Arts, Dancing and Sports Dataset: a Challenging Stereo and Multi-View Dataset for 3D Human Pose Estimation.
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Image and Vision Computing, 61:22-39, May 2017. [supplemental] - Learning word embeddings via context grouping.
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In: ACM Turing 50th Celebration Conference - China, Shanghai, May 2017. - Efficient tree-structured SfM by RANSAC generalized Procrustes analysis.
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Computer Vision and Image Understanding (CVIU), 157:179-189, April 2017. - Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation.
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International Journal of Computer Vision (IJCV), 122(1):149-168, March 2017. - DynamicManga: Animating Still Manga via Camera Movement.
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IEEE Trans. on Multimedia (TMM), 19(1):160-172, Jan 2017. [supplemental | video] - Approximate Inference for Generic Likelihoods via Density-Preserving GMM Simplification.
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In: NIPS 2016 Workshop on Advances in Approximate Bayesian Inference, Barcelona, Dec 2016.
Recent Project Pages [more]
We collect a multi-view and stereo-depth dataset for 3D human pose estimation, which consists of challenging martial arts actions (Tai-chi and Karate), dancing actions (hip-hop and jazz), and sports actions (basketball, volleyball, football, rugby, tennis and badminton).
- , "Martial Arts, Dancing and Sports Dataset: a Challenging Stereo and Multi-View Dataset for 3D Human Pose Estimation." Image and Vision Computing, 61:22-39, May 2017. [supplemental]
We use hidden Markov models (HMMs) to analyze eye movement data. A person’s eye fixation sequence is summarized with an HMM, and common strategies among people are discovered by clustering HMMs.
- , "Understanding eye movements in face recognition using hidden Markov models." Journal of Vision, 14(11):8, Sep 2014.
We present an approach that allows web designers to easily direct user attention via visual flow on web designs.
- , "Directing User Attention via Visual Flow on Web Designs." ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2016), Dec 2016. [supplemental | video]
