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» Algorithmic Fusion for More Robust Feature Tracking
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SIGIR
2006
ACM
15 years 3 months ago
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large ...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
CVPR
2007
IEEE
15 years 11 months ago
Metropolis-Hasting techniques for finite-element-based registration
In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis...
Adeline M. M. Samson, Frédéric J. P....
ICML
2006
IEEE
15 years 10 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
FGR
1998
IEEE
170views Biometrics» more  FGR 1998»
15 years 1 months ago
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression
Current approaches to automated analysis have focused on a small set of prototypic expressions (e.g., joy or anger). Prototypic expressions occur infrequently in everyday life, ho...
Jeffrey F. Cohn, Adena J. Zlochower, James Jenn-Ji...
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CVPR
2007
IEEE
15 years 11 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