Mutual Information Based Stereo Correspondence in Extreme Cases

Stereo correspondence is an ill-posed problem mainly due to matching ambiguity, which is especially serious in extreme cases where the corresponding relationship is unknown and can be very complicated. Mutual information (MI), which assumes no prior relationship on the matching pair, is a good solution to this problem. This paper proposes a context-aware mutual information and Markov Random Field (MRF) based approach with gradient information introduced into both the data term and the smoothness term of the MAP-MRF framework where such advanced techniques as graph cuts can be used to find an accurate disparity map. The results show that the proposed context-aware method outperforms non-MI and traditional MI-based methods both quantitatively and qualitatively in some extreme cases.