Learning and Inference in Low-Level Vision
published: Jan. 19, 2010, recorded: December 2009, views: 1407
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Low level vision addresses the issues of labeling and organizing image pixels according to scene related properties - such as motion, contrast, depth and reﬂectance. I will describe our attempts to understand low-level vision in humans and machines as optimal inference given the statistics of the world. If time permits, I will discuss my favorite NIPS rejected papers. Yair Weiss is an Associate Professor at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from MIT working with Ted Adelson on motion analysis and did postdoctoral work at UC Berkeley. Since 2005 he has been a fellow of the Canadian Institute for Advanced Research. With his students and colleagues he has co-authored award winning papers in NIPS (2002),ECCV (2006), UAI (2008) and CVPR (2009).
Slide presentation contains animation videos which can be found at http://www.cs.huji.ac.il/~yweiss/movies.tar.gz.
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