Disparity map restoration by integration of confidence in Markov random fields models
نویسندگان
چکیده
This paper proposes some Markov Random Field (MRF) models for restoration of stereo disparity maps. The main aspect is the use of confidence maps provided by the Symmetric Multiple Windows (SMW) stereo algorithm to guide the restoration process. The SMW algorithm is an adaptive, multiple-window scheme using left-right consistency to compute disparity and its associated confidence in presence of occlusions. The MRF approach allows to combine in a single functional all the available information: observed data with its confidence, noise, and a-priori hypotheses. Optimal estimates of the disparity are obtained by minimizing an energy functional using simulated annealing. Results with a real stereo pair show the improvement obtained by the restoration using a MRF approach integrating confidence data.
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