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Springer, Lecture Notes in Computer Science, p. 818-825, 2005

DOI: 10.1007/11553595_100

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Optimal Parameter Estimation for MRF Stereo Matching

Proceedings article published in 2005 by Riccardo Gherardi, Umberto Castellani, Andrea Fusiello, Vittorio Murino ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

This paper presents an optimisation technique to select automatically a set of control parameters for a Markov Random Field applied to stereo matching. The method is based on the Reactive Tabu Search strategy, and requires to define a suitable fitness function that measures the performance of the MRF stereo algorithm with a given parameters set. This approach have been made possible by the recent availability of ground-truth disparity maps. Experiments with synthetic and real images illustrate the approach.