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]
- Eye Movement Patterns in Face Recognition are Associated with Cognitive Decline in Older Adults.
Psychonomic Bulletin & Review, to appear 2018.
- Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference.
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2018.
- Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking.
IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), to appear 2018.
- Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes.
In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, to appear, Jun 2018.
- Color Orchestra: Ordering Color Palettes for Interpolation and Prediction.
IEEE Transactions on Visualization and Computer Graphics (TVCG), 24(6):1942-1955, June 2018.
- Scanpath modeling and classification with Hidden Markov Models.
Behavior Research Methods, 50:362-379, Feb 2018.
- Incorporating Side Information by Adaptive Convolution.
In: Neural Information Processing Systems, Long Beach, Dec 2017. [supplemental]
- Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.
Vision Research, 141:204-216, Dec 2017.
- Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures.
Cognition, 169:102-117, Dec 2017.
- Mining Probabilistic Color Palettes for Summarizing Color Use in Artwork Collections.
In: Symposium on Visualization, SIGGRAPH Asia 2017, Bangkok, Nov 2017. [supplemental]
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],