Disparity Map Estimation Using A Total Variation Bound

Authors:
Wided Miled;Jean Christophe Pesquet
Affiliations:
Université Marne-la-Vallee, Champs-sur-Marne, France;Université Marne-la-Vallee, Champs-sur-Marne, France
Venue:
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Year:
2006

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Abstract

This paper describes a new variational method for estimating disparity from stereo images. The stereo matching problem is formulated as a convex programming problem in which an objective function is minimized under various constraints modelling prior knowledge and observed information. The algorithm proposed to solve this problem has a block-iterative structure which allows a wide range of constraints to be easily incorporated, possibly taking advantage of parallel computing architectures. In this work, we use a Total Variation bound as a regularization constraint, which is shown to be well-suited to disparity maps. Experimental results for standard data sets are presented to illustrate the capabilities of the proposed disparity estimation technique.