标题：OCCLUSION-AIDED SUPPORT WEIGHTS FOR LOCAL STEREO MATCHING
作者：WEI WANG;CAIMING ZHANG;SHUOZHEN WANG;XUEMEI LI
作者机构：School of Computer Science and Technology Shandong University, Jinan 250101, P. R. China;School of Computer Science and Technolo
来源：International journal of pattern recognition and artificial intelligence
关键词：stereo vision;local stereo matching;occlusion detection;adaptive support-weight;disparity calibration
摘要：There has been a significant improvement in stereo matching with the introduction of adaptive support weights. Existing local methods mainly focus on the computation of support weight which is critical in cost aggregation and usually get excellent results. However, the negative effects of occluded regions are often ignored, which results in the problem of foreground fattening and blurred depth borders. This paper proposes a novel support aggregation strategy by utilizing the occlusion information obtained from left-right consistency check. The weights of invalid points are noticeably reduced at each disparity estimation stage. Experimental results on the Middlebury images show that our method is highly effective in improving the disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed method achieves the best performance among all the local methods. Moreover, our approach can be easily integrated into nearly all the existing support weights strategies.