Fast and robust semi-local stereo matching using possibility distributions

Authors:
Haythem Ghazouani;Moncef Tagina;Rene Zapata
Affiliations:
National School for Computer Studies, SOIE Laboratory, Campus Universitaire de La Manouba, La Manouba 2010, Tunisia.;National School for Computer Studies, SOIE Laboratory, Campus Universitaire de La Manouba, La Manouba 2010, Tunisia.;LIRMM Laboratory, University of Montpellier II, 4-6-7, 161 rue Ada, 34392 Montpellier Cedex 5, France
Venue:
International Journal of Computational Vision and Robotics
Year:
2011

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Abstract

Global stereo matching methods aim to reduce the sensibility of stereo correspondence to ambiguities caused by occlusions, poor local texture or fluctuation of illumination. However, when facing the problem of real-time stereo matching, as in robotic vision, local algorithms are known to be the best. In this paper, we propose a semi-local stereo matching algorithm (SLSM algorithm); an area-based method that embodies global matching constraints in the matching score. Our approach uses a fuzzy formularisation of the similarity assumption in order to define a matching possibility distribution. An unmatching possibility distribution is defined by applying global constraints to the matching possibility distribution. The final matching cost is computed using the two possibility distributions. Experimental results and comparison with other existing algorithms are presented to demonstrate the performance and effectiveness of our approach.