摘要
为了解决传统非局部立体匹配算法在纹理丰富区域匹配误差较大的问题,提出基于颜色和边缘信息的非局部立体匹配算法。代价计算阶段,结合灰度和梯度信息求得匹配代价。代价聚合阶段,为降低相似背景下的误匹配率,利用最小生成树进行代价聚合,结合颜色和边缘信息重新定义权重函数。再利用胜者为王(WTA)策略求得最佳视差,通过左右一致性检验和中值滤波等后处理操作对视差图作精细化处理。最后在Middlebury数据平台上对算法进行可行性验证,实验结果表明,图像的平均误匹配率由原算法的6.02%降低到5.10%。
This study proposes an alternative non-local stereo matching algorithm based on color and edge information to reduce the large matching error in the texture-rich regions of the traditional non-local stereo matching algorithm.In the cost computation stage,the gray and gradient information are combined to obtain the matching cost of pixels.In the cost aggregation stage,to reduce the mismatch rate for regions with a similar background,the minimum spanning tree is used for cost aggregation,and the weight function is redefined through combination of color and edge information.Then,the winner-take-all(WTA)strategy is implemented to obtain optimal disparity,and the disparity map is refined through post-processing operations such as left-right consistency checking and median filtering.Finally,the feasibility of the proposed algorithm is verified using the Middlebury data platform.Experimental results show that compared with the traditional algorithm,the proposed algorithm reduces the average mismatch rate of the image from 6.02%to 5.10%.
作者
马晴晴
王彩芳
Ma Qingqing;Wang Caifang(College of Arts and Sciences,Shanghai Maritime University,Shanghai 201306,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第10期196-202,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(11401372)。
关键词
图像处理
非局部立体匹配算法
跨尺度模型
权重函数
最小生成树
image processing
non-local stereo matching algorithm
cross-scale model
weight function
minimum spanning tree
