New disparity map estimation using higher order statistics

This paper presents a new algorithm of disparity map estimation. The originality of this method lies in the process of dense disparity map estimation using dynamic programming constrained by interest points and using the higher order statistics (HOS) criteria for matching noisy images. Experiments with noisy real images have validated our method and have clearly shown the improvement over the existing ones. The dense disparity map obtained is more reliable when compared to the similar second-order statistics (SOS)-based dynamic programming and HOS-based correlation methods