Title :
Bayesian Stereo Matching
Author :
Cheng, Li ; Caelli, Terry
Author_Institution :
University of Alberta, Edmonton, Canada
Abstract :
In this paper we explore a Bayesian framework for inferring the disparity map from an image pair. Markov Chain Monte Carlo sampling techniques are employed for learning the hyper-parameters which control two robust statistical functions for modelling the specific image pair; and loopy belief propagation is used for approximate inference of the MAP disparity map. Encouraging results are obtained on a standard set of image pairs.
Keywords :
Bayesian methods; Belief propagation; Hidden Markov models; Image sampling; Inference algorithms; Least squares approximation; Markov random fields; Monte Carlo methods; Robust control; Stereo vision;
Conference_Titel :
Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference on
DOI :
10.1109/CVPR.2004.33
