针对传统非参数变换的局限性,提出了一种立体匹配算法。利用参考图像的色彩分割信息获得基于任意形状和大小支持区域的匹配代价,并在相似色彩区域内计算加权非参数变换匹配代价,再将这两种匹配代价融合构成联合匹配代价,通过局部优化方法获得稠密视差图。实验结果表明算法提高了低纹理区域、遮挡区域和不连续区域的匹配精度,并对幅度失真具有鲁棒性。
Aiming at the limitations of the traditional non-parametric transformation, a stereo matching algorithm is proposed. One matching cost based on support region with arbitrary shape and size is obtained by using color segmentation information of the reference image, and another matching cost of weighted non-parametric transform in similar color region is obtained, then joint matching cost is obtained by the fusion of the both matching cost, and the dense disparity map is realized through the local optimization method. The experiment results show that the algorithm can improve the accuracy of the low texture, borders and occlusion regions. At the same time, it is robust against the amplitude distortion.