Disparity estimation using color coherence and stochastic diffusion

This paper deals with disparity estimation based on Markov random field (MRF) models and color coherence. The disparity and line fields are explicitly modeled as MRFs, and are estimated by the stochastic diffusion. The potential functions are defined from the novel stochastic models between disparity and line fields. The color information is also utilized to model the textureless regions where the disparity estimation generally fails. And, the derived potential functions are minimized by the novel energy minimization method called stochastic diffusion. The stochastic diffusion diffuses the potential space using the probability distribution of neighboring fields, and searches for the optimal fields in the converged potential space. Some experiments show good performances of disparity estimation, which are compared with the other methods in a Webpage.