Abstract. Sparse signal models learned from data are widely used in audio, image, and video restoration. They have recently been generalized to discriminative image understanding t...
Julien Mairal, Marius Leordeanu, Francis Bach, Mar...
We propose a graph-based semi-supervised symmetric matching framework that performs dense matching between two uncalibrated wide-baseline images by exploiting the results of sparse...
Jianxiong Xiao, Jingni Chen, Dit-Yan Yeung, Long Q...
Abstract. A novel algorithm is presented for the 3D reconstruction of human action in long (> 30 second) monocular image sequences.A sequence is represented by a small set of au...
Gareth Loy, Martin Eriksson, Josephine Sullivan, S...
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Abstract. Establishing correspondence between features of a set of images has been a long-standing issue amongst the computer vision community. We propose a method that solves the ...