Adaptive Support-Weight Stereo-Matching Approach with Two Disparity Refinement Steps

Abstract

ABSTRACTMachine vision is used to reconstruct three-dimensional information from images. To perceive depth information of target objects, many techniques in machine vision such as camera calibration, epipolar lines rectification, stereo matching, etc. are considered. In the stereo-matching field which is regarded as a critical step, local and global stereo-matching methods are the two key methods to get the disparity image which is used to calculate the depth information. A local method – Adaptive Support-Weight (ASW) algorithm which has high efficiency and simple structure can obtain comparable accuracy of results with the global method. But in the final disparity image obtained by ASW, there are still some significant mismatching areas. In this paper, ASW and two disparity refinement steps are proposed to refine the accuracy of the disparity image. The mismatching areas in the disparity image are corrected by the two disparity refinement steps which mainly include the variable-cross region, disparity inheritance, and fixed window voting methods. Then, the performance of the proposed algorithm under different parameters is analyzed based on the Middlebury benchmark. Finally, the experimental results show that the accuracy of the disparity image is improved after the disparity refinement steps and the final refined result is comparable to some stereo-matching algorithms.