Towards correlation-based matching algorithms that are robust near occlusions

In the context of computer vision, matching can be done using correlation measures. This paper presents new algorithms that use two correlation measures: the zero mean normalised cross-correlation, ZNCC, and the smooth median absolute deviation, SMAD. While ZNCC is efficient in non-occluded areas and non-robust near occlusions, SMAD is non-efficient in non-occluded areas and robust near occlusions. The aim is to use the advantages of ZNCC and SMAD to deal with the problem of occlusions and to obtain dense disparity maps. The experimental results show that these algorithms are better than the ZNCC-based algorithm and SMAD-based algorithm.