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CVPR
2005
IEEE
14 years 6 months ago
Combining Object and Feature Dynamics in Probabilistic Tracking
Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image fea...
Leonid Taycher, John W. Fisher III, Trevor Darrell
BMVC
2002
13 years 7 months ago
Low Density Feature Point Matching for Articulated Pose Identification
We describe a general algorithm for identifying an arbitrary pose of an articulated subject with low density feature points. The algorithm aims to establish a one-to-one correspon...
Horst Holstein, Baihua Li
CVPR
2007
IEEE
14 years 6 months ago
Cast Shadow Removal Combining Local and Global Features
In this paper, we present a method using pixel-level information, local region-level information and global-level information to remove shadow. At the pixel-level, we employ GMM t...
Zhou Liu, Kaiqi Huang, Tieniu Tan, Liangsheng Wang
ICCV
2009
IEEE
13 years 2 months ago
SURF Tracking
Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motionbased trackin...
Wei He, Takayoshi Yamashita, Hongtao Lu, Shihong L...