光学学报, 2019, 39 (3): 0315001, 网络出版: 2019-05-10
基于图像分割的稠密立体匹配算法 下载: 1080次
Dense Stereo Matching Algorithm Based on Image Segmentation
机器视觉 立体匹配算法 匹配代价计算方法融合 十字交叉自适应窗口加权中值滤波 machine vision stereo matching algorithm matching cost computation method fusion cross adaptive window weighted median filtering
摘要
提出一种基于图像分割的稠密立体匹配算法,该算法将灰度-梯度算法与零均值归一化互相关(ZNCC)算法相结合生成匹配代价,利用SLIC(Simple Liner Iterative Cluster)算法对图像进行分割,基于视差图和超像素更新了匹配代价。在视差后处理阶段,基于左右一致性检验(LRC)、孔洞填充和十字交叉自适应窗口加权中值滤波的方法减小视差图的误匹配率。利用Middlebury数据集的4组图像进行测试,测试结果表明,平均误匹配率为4.99%。
Abstract
A dense stereo matching algorithm is proposed based on image segmentation. This algorithm combines the gray-gradient algorithm and the zero-mean normalized cross-correlation (ZNCC) algorithm to generate matching cost. The SLIC (Simple Liner Iterative Cluster) algorithm is used for image segmentation. A method based disparity map and superpixels is proposed to update the matching cost. At the disparity post-processing stage, the LRC (Left Right Check), hole filling and cross adaptive window weighted median filtering methods are used to reduce the error matching rate of the disparity map. The performance evaluation experiments on four Middlebury stereo pairs demonstrate that the proposed algorithm achieves an average error matching rate of 4.99%.
马瑞浩, 朱枫, 吴清潇, 鲁荣荣, 魏景阳. 基于图像分割的稠密立体匹配算法[J]. 光学学报, 2019, 39(3): 0315001. Ruihao Ma, Feng Zhu, Qingxiao Wu, Rongrong Lu, Jingyang Wei. Dense Stereo Matching Algorithm Based on Image Segmentation[J]. Acta Optica Sinica, 2019, 39(3): 0315001.