Stereo matching by using a new local descriptor based on sign and order in belief propagation algorithm

Abstract

Stereo matching is still a challenging problem in computer vision. Belief propagation (BP) algorithm is one of the global methods to solve this problem which can lead to desired results if useful information is applied in data function of this algorithm. In most literatures, color information of pixels has been applied in data function of BP which its result is not very well. In this paper, a low complexity modified version of local descriptor based on contrast and ordering (LCO) of intensity differences is proposed which is known as the local sign and ordering (LSO). LSO, like its original descriptor, has some distinguished properties such as robustness against illumination change and preserving depth discontinuity which are the important challenges of stereo matching. Moreover, a combination of intensity values of pixels and LSO descriptor is presented. This combination of cost measures is utilized as data function of BP algorithm. In the post-processing of stereo matching, the left–right consistency check is applied to the final result, which is followed by median filter in order to refine disparity map. Performed experiments on the Middlebury dataset show that the results of the proposed method are competitive with the methods based on other descriptors used in BP algorithm. Furthermore, the proposed method has relatively good performance compared to the other methods rather than BP algorithm.