This paper presents a generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solvi...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Image matting deals with finding the probability that each pixel in an image belongs to a user specified `object' or to the remaining `background'. Most existing methods...
Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/tracking in comparison to leading holistic approaches (e.g., AAMs). A maj...
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...