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CVPR
2007
IEEE
16 years 1 months ago
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
Pablo Arias, Gregory Randall, Guillermo Sapiro
CVPR
2008
IEEE
16 years 1 months ago
Joint tracking of features and edges
Sparse features have traditionally been tracked from frame to frame independently of one another. We propose a framework in which features are tracked jointly. Combining ideas fro...
Stanley T. Birchfield, Shrinivas J. Pundlik
ICCV
2003
IEEE
16 years 1 months ago
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimatio...
Changjiang Yang, Ramani Duraiswami, Nail A. Gumero...
ECCV
2006
Springer
16 years 1 months ago
Accelerated Convergence Using Dynamic Mean Shift
Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this pa...
Kai Zhang, James T. Kwok, Ming Tang
ICIP
2008
IEEE
16 years 25 days ago
A supervised nonlinear neighborhood embedding of color histogram for image indexing
Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem o...
Xian-Hua Han, Yen-Wei Chen, Takeshi Sukegawa