A Dense Disparity Estimation Method using Color Segmentation and Energy Minimization

This paper presents a new dense disparity estimation method using color segmentation and energy minimization. Segmentation-based methods have an advantage of clear representation of disparities on discontinuous region. This paper remodels the energy minimization method to fit with segmentation-based method. In the proposed method, the initial disparity map is obtained by variable block matching of the segmented plane and random sample consensus (RANSAC) fitting. Then energy minimization is performed on the small segmented planes and the overall segmented planes, respectively to refine the disparity map more accurately. At the end of the method, erroneous stripes that are not corrected by the segmentation-based approach are processed by a pixel-based method. Experimental results show that the error rate of the proposed method is comparable to other state-of-the-art matching methods on various stereo image pairs.