Despite much research on patch-based descriptors, SIFT remains the gold standard for finding correspondences across images and recent descriptors focus primarily on improving spe...
Modeling representations of image patches that are quasi-invariant to spatial deformations is an important problem in computer vision. In this paper, we propose a novel concept, t...
Jan Ernst, Maneesh Kumar Singh, Visvanathan Ramesh
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
We consider regions of images that exhibit smooth statistics, and pose the question of characterizing
the “essence” of these regions that matters for visual recognition. Ideal...
Ganesh Sundaramoorthi (UCLA), Peter Petersen (UCLA...
Prominent feature point descriptors such as SIFT and
SURF allow reliable real-time matching but at a compu-
tational cost that limits the number of points that can be
handled on...
Michael Calonder, Vincent Lepetit, Pascal Fua, Kur...