基于水平树结构的可变权重代价聚合立体匹配算法

Variable Weight Cost Aggregation Algorithm for Stereo Matching Based on Horizontal Tree Structure

针对基于树结构的代价聚合方法中只利用颜色信息选择权值支持区域时,在图像边界区域易产生误匹配的问题,提出了一种基于水平树结构的可变权重代价聚合立体匹配算法。采用水平树代价聚合得到初始视差值,结合初始视差值与颜色信息重构水平树,在更新后的树结构上进行代价聚合,得到最终视差图。在视差后处理阶段,提出了一种改进的非局部视差后处理算法,将不满足左右一致性匹配的像素点引入匹配代价量构造中,提高了最终视差图的匹配精度。在Middlebury数据集的31对图像上进行测试,结果表明,未进行视差后处理时所提算法在未遮挡区域的平均误匹配率为6.96%,代价聚合平均耗时1.52s。

In the cost aggregation methods based on tree structure,the weight support region is selected only by color information,and therefore it is easy to produce mismatching problem in the image boundary area.A variable weight cost aggregation algorithm for stereo matching based on horizontal tree structure is proposed to solve the problem.The initial disparity value is obtained by the cost aggregation of horizontal tree,the horizontal tree is reconstructed with initial disparity value and color information,and the final disparity map is obtained on the updated tree structure by cost aggregation.In the disparity refinement stage,an improved non-local disparity refinement algorithm is proposed with the pixel points that do not satisfy left-right consistency constraint introduced into the matching cost volume,which improves the matching accuracy of final disparity map.Performance evaluation experiments on all 31 Middlebury stereo pairs demonstrate that the proposed algorithm achieves an average error matching rate of 6.96% in the non-occluded areas without disparity refinement,and the cost aggregation takes 1.52 son average.