Greg MoriAssociate Professor
School of Computing Science
Simon Fraser University
8888 University Drive
CANADA V5A 1S6
Office: TASC1 8007
Phone: (778) 782-7111
Fax: (778) 782-3045
Ph.D. in Computer Science, University of California at Berkeley, 2004.
Hon. B.Sc. in Computer Science and Mathematics, University of Toronto, 1999.
My research is in computer vision, and is concerned with developing
algorithms that automatically interpret images and videos,
particularly those containing people. I have made significant
contributions towards solving the problems of human pose estimation
and human action recognition. At a broad level, the methodology
followed is to construct features and representations that capture our
intuition regarding these vision problems. We operationalize these via
machine learning algorithms, adapting them to suit our
Specific examples of features and representations include work on superpixels for representing images, motion features for human action recognition, and our bag-of- words model for video sequences. We have developed variants of machine learning algorithms for models such as the hidden Conditional Random Field (hCRF) and Latent Dirichlet Allocation (LDA) to implement these ideas.
Research interests keywords:
|Nataliya Shapovalova (PhD) had a paper accepted to Neural Information Processing Systems (NIPS) 2013. A joint model for visual saliency and action recognition in videos is described, trained in a latent SVM framework utilizing eye gaze data.|
|Guang-Tong Zhou (PhD), Tian Lan (PhD), and Arash Vahdat (PhD) had a paper accepted to Neural Information Processing Systems (NIPS) 2013. The paper describes an approach for clustering with latent variables under a max-margin criterion, with application to videos of human activities.|
|Tian Lan successfully defended his Ph.D. thesis From Flat to Hierarchical: Modeling Structures in Visual Recognition. Congratulations Tian!|
|Arash Vahdat (PhD) had a paper accepted to IEEE International Conference on Computer Vision (ICCV), 2013. An algorithm for predicting a set of tags that describe and image or video is described, trained from noisy tag annotations.|
|Arash Vahdat (PhD) and Kevin Cannons (PDF) had a paper accepted to IEEE International Conference on Computer Vision (ICCV), 2013. A compositional model for video event recognition is presented, based on a novel multiple kernel learning algorithm that incorporates latent variables.|
|Tian Lan (PhD) had a paper accepted to IEEE International Conference on Computer Vision (ICCV), 2013. A multi-level object detection framework that models sub-categories through to visual composites is developed.|
|Hossein Hajimirsadeghi (PhD) and Jinling Li (MSc) had a paper accepted to the Conference on Uncertainty in Artificial Intelligence (UAI), 2013. A multiple instance learning algorithm is developed, which models varying levels of positive instances in a bag. A discriminative training algorithm is proposed, based on efficient inference of cardinality-based clique potential functions.|
|Tian Lan (PhD) had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2013. This work develops an image tag ranking algorithm that can determine which keywords are more relevant to a given image.|
|Guang-Tong Zhou (PhD), Tian Lan (PhD), and Weilong Yang had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2013. An image classification algorithm that matches a set of objects to an image is developed.|
|Yuke Zhu (BSc) and Tian Lan (PhD) had a paper on activity recognition accepted to IAPR Conference on Machine Vision Applications (MVA), 2013. Latent variable models were used to analyze nursing home surveillance video.|
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