Dr. Malik and his colleagues have developed a theory of object recognition by measuring shape similarity, using dense point correspondences based on robust relational descriptors: ``shape contexts.. and "geometric blur templates". He will show results on a variety of 2D and 3D recognition problems.

The action recognition problem is that of finding instances of actions in video sequences: run, jump, kick etc. This has to be accomplished while allowing for variation in the person performing the action, clothing, illumination and viewpoint. Dr. Malik has developed two approaches to recognition of actions that will be discussed.

This talk is based on joint work; please visit http://www.eecs.berkeley.edu/Research/Projects/CS/vision/vision_group.html for pointers to publications.

His research interests are in computer vision and computational modeling of human vision. His work spans a range of topics in vision including image segmentation and grouping, texture, stereopsis, object recognition, image based modeling and rendering, content based image querying, and intelligent vehicle highway systems. He has authored or co-authored more than a hundred research papers on these topics.

He received the Diane S. McEntyre Award for Excellence in Teaching from the Computer Science Division, University of California at Berkeley, in 2000. He was awarded a Miller Research Professorship in 2001. He serves on the Editorial Board of the International Journal of Computer Vision.