基于自适应权重的遮挡信息立体匹配算法

针对传统局部立体匹配算法在深度不连续区域误匹配率高的问题，提出一种基于自适应权重的遮挡信息立体匹配算法。首先，采用左右一致性检测算法检测参考图像与目标图像的遮挡区域；然后利用遮挡信息，在代价聚合阶段降低遮挡区域像素点所占权重，在视差优化阶段采用扫描线传播方式选择水平方向最近点填充遮挡区域的视差；最后，根据Middlebury数据集提供的标准视差图为视差结果计算误匹配率。实验结果表明，基于自适应权重的遮挡信息匹配算法相对于自适应权重算法误匹配率降低了16%，并解决了局部立体匹配算法在深度不连续区域误匹配率高的问题，提高了算法的匹配精确性。

Occlusion information stereo matching approach based on adaptive weight

Aiming at the high mismatching rate of traditional local stereo matching algorithm at the depth discontinuities, this paper proposed an occlusion information stereo matching approach based on adaptive weight. Firstly, left-right consistency check algorithm detected the occlusion area of the reference image and the target image. Then, this paper utilized occlusion information to reduce the weight of pixels in the occlusion area at the process of cost aggregation, and scanline propagation filled occlusion pixels with horizontally nearest points in the disparity refinement stage. Finally, it calculated the mismatching rate of disparity map according to the Middlebury standard image dataset. The experimental results show that compared with adaptive support weight approach, the occlusion information stereo matching approach based on adaptive weight decreases the mismatching rate by 16%. Meanwhile, the proposed algorithm not only has lower mismatching rate in the depth discontinuous region, but also improves the accuracy of algorithm matching.