Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in l...
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure...
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection o...
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...
—This paper addresses the problem of dense disparity estimation from a pair of color stereo images. Based on a convex set theoretic formulation, the stereo matching problem is ca...