中国科学院大学学报2025，Vol.42Issue(6)：814-822,9.DOI:10.7523/j.ucas.2023.081
结合超像素分割与引导滤波的图像密集匹配算法
Image dense matching algorithm combining superpixel segmentation and guided filtering
摘要
Abstract
In order to solve the problem that the existing local stereo matching method has low matching accuracy in the discontinuous region of parallax,a dense matching method combining superpixel segmentation and guided filtering is proposed in this paper.Firstly,a feature matching method is used to determine the disparity range,and the zero-mean normalized cross correlation is combined with gray-level and gradient information to construct the cost function.Secondly,the label map after superpixel segmentation is used to constrain the adaptive changes of the guided filtering window shape,and the cost is aggregated.Finally,the aggregation cost is used as the data item to construct the global energy function,and the disparity map is solved by graph cut algorithm,and multi-step disparity optimization is performed on the disparity map.Experimental results show that the average mismatching rate of the proposed method is 4.8％on the standard test image set provided by Middlebury website,which is significantly better than the traditional guided filtering dense matching method and semi-global matching method.关键词
机器视觉/密集匹配/超像素分割/引导滤波/图割算法Key words
machine vision/dense matching/superpixel segmentation/guided filtering/graph cut algorithm分类
信息技术与安全科学引用本文复制引用
张政,章文毅,许殊..结合超像素分割与引导滤波的图像密集匹配算法[J].中国科学院大学学报,2025,42(6):814-822,9.基金项目
中国科学院空间科学战略性先导科技专项(XDA15040300)资助 （XDA15040300）
