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Abstract: We present an new robust feature-based stereo-matching method. Matching is first formulated as an integer programming problem allowing for missing and spurious features. The search domain is then relaxed to a class of compact convex sets whose extreme points belong to the original set of valid correspondences. This way we convert the optimization problem to a continuous domain, and we can guarantee that any convex (concave) criterion function constrained to this domain is maximized (minimized)... (Update)
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BibTeX entry: (Update)
@misc{ maciel-robust,
author = "Joćo Maciel and Joćo Costeira",
title = "Robust Stereo Matching as a Concave Minimization Problem",
url = "citeseer.ist.psu.edu/article/maciel99robust.html" }
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