Abstract: In this paper, we cast stereo matching as a problem in merit maximization. This is achieved by the formulation of a merit function which influences the similarity between primitives in the right and left images and the mutual dependency between primitives. Stereo matching is done by finding the `best' paths that maximize the merit function. This is handled by using the dynamic programming technique. With this algorithm, a global optimum matching can be obtained. We give a mathematical description for the merit function and the algorithm has been implemented. The experimental results are presented to show the efficacy of the proposed stereo matching method. !10
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