Bayesian Stereo Matching

Authors:
Li Cheng;Terry Caelli
Affiliations:
University of Alberta, Edmonton, Canada;University of Alberta, Edmonton, Canada
Venue:
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Year:
2004

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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.