Dense Disparity Map Estimation Using CUMULANTS
Mohammed Rziza, Driss Aboutajdine · 2001
We present a new efficient stereo algorithm addressing robust disparity estimation in the presence of the noises. Originality of this method is a dense disparity map estimation using the dynamic programming constrained by interest points and using Higher Order Statistics (HOS) (Cumulant) criteria for matching noisy images. Hierarchical scheme is used to ameliorate HOS-based correlation method. Experiments with both synthetic and 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. Key words: epipolar geometry, rectification, disparity, matching, correlation, dynamic programming, constrained dynamic programming, Higher Order Statistics (Cumulant). I.