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Announcements

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【2010-12-27】Learning to track articulated objects

非專題討論演講公告
Poster:Post date:2010-12-25


Title: Learning to track articulated objects
Speaker:Prof Ming-Husan Yang
(University of California, Merced)
Time: 14:30-15:20, Dec 27 (Mon), 2010
Place: Room 102, CSIE Building

Abstract:

In this talk, I will present two lines of research for tracking articulated objects in different scenarios. For images acquired at a distance, we propose an algorithm for accurate tracking of articulated objects using online update of appearance and shape. The challenge here is to model foreground appearance with histograms in a way that is both efficient and accurate. In this algorithm, the constantly changing foreground shape is modeled as a small number of rectangular blocks, whose positions within the tracking window are adaptively determined. Under the general assumption of stationary foreground appearance, we show that robust object tracking is possible by adaptively adjusting the locations of these blocks.


Short-bio:
Ming-Hsuan Yang is an assistant professor in EECS at University of California, Merced. He received the PhD degree in computer science from University of Illinois at Urbana-Champaign in 2000. Prior to joining UC Merced, he was a senior research scientist at Honda Research Institute working on vision problems related to humanoid robots. In 1999, he received the Ray Ozzie fellowship for his research work. He coauthored the book Face Detection and Gesture Recognition for Human-Computer Interaction_ _(Kluwer Academic 2001) and is one of the guest editors for a special issue on face recognition for Computer Vision and Image Understanding, 2003 as well as a special issue on real-world face recognition for IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011. He serves in different capacities for a few conferences (area chair for ICCV 2011, AAAI 2011, FG 2011, ACCV 2010, CVPR 2009, ACCV 2009, CVPR 2008, and publication chair for CVPR 2010). He is an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence, and Image and Vision Computing.



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