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.
Behavior Research Methods, to appear.
- Color Orchestra: Ordering Color Palettes for Interpolation and Prediction.
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.
Vision Research, to appear, 2017.
- 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]
- Efficient tree-structured SfM by RANSAC generalized Procrustes analysis.
Computer Vision and Image Understanding (CVIU), 157:179-189, April 2017.
- Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation.
International Journal of Computer Vision (IJCV), 122(1):149-168, March 2017.
- DynamicManga: Animating Still Manga via Camera Movement.
IEEE Trans. on Multimedia (TMM), 19(1):160-172, Jan 2017. [supplemental | video]
- Approximate Inference for Generic Likelihoods via Density-Preserving GMM Simplification.
In: NIPS 2016 Workshop on Advances in Approximate Bayesian Inference, Barcelona, Dec 2016.
- Directing User Attention via Visual Flow on Web Designs.
ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2016), Dec 2016. [supplemental | video]
- Counting People Crossing a Line using Integer Programming and Local Features.
IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), 26(10):1955-1969, Oct 2016. [appendix]
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],