Patchmatch-Based Robust Stereo Matching Under Radiometric Changes.

In the real world, the two challenges of stereo vision system include a robust system under various radiometric changes and real-time process. To extract depth information from stereoscopic images, this paper proposes Patchmatch-based robust and fast stereo matching under radiometric changes. For this, a cost function was designed and minimized for estimating an accurate disparity map. Specifically, we used a prior probability to minimize the occlusion region and a smoothness term that considers convexity of objects to extract a fine disparity map. For evaluating the performance of the proposed scheme, we used Middlebury stereo data sets with radiometric changes. The experimental result showed that the proposed method outperforms state-of-the-art methods by up to 3.35 percent better and a range of 4.71 - 27.24 times faster result in terms of bad pixel error and processing time, respectively. Therefore, we believe that the proposed scheme can be a useful tool for computer vision-based applications.