Object segmentation based on accurate disparity estimation

Object segmentation is an important issue in computer vision. In this paper, we present a new object segmentation algorithm based on accurate disparity map. First, the accurate disparity map is available by a stereo match algorithm including initial matching cost estimation, mismatched pixels checking, plane estimation and self-adaptive hierarchical belief-propagation. Then, self-adaptive threshold segmentation is performed on the result that is achieved from the first step. Experimental results show that the proposed algorithm is an effective object extraction method suitable for stereoscopic static scenes and image sequences with unitary global motion.