结合超像素分割与引导滤波的图像密集匹配算法*

doi:10.7523/j.ucas.2023.081

中国科学院大学学报

结合超像素分割与引导滤波的图像密集匹配算法^*

张政^1,2, 章文毅^1,†, 许殊¹

1 中国科学院空天信息创新研究院,北京 100094;
2 中国科学院大学,北京 100049

收稿日期:2023-04-21 修回日期:2023-10-09 发布日期:2023-11-03
通讯作者: † E-mail：wyzhang@rsgs.ac.cn
基金资助:
*中国科学院空间科学战略性先导科技专项(XDA15040300)资助

Image dense matching algorithm combining superpixel segmentation and guided filtering

ZHANG Zheng^1,2, ZHANG Wenyi¹, XU Shu¹

1 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;
2 University of Chinese Academy of Sciences,Beijing 100049,China

Received:2023-04-21 Revised:2023-10-09 Published:2023-11-03

摘要/Abstract

摘要： 针对现有局部立体匹配方法在视差不连续区域匹配精度较低的问题,该文提出了一种结合超像素分割与引导滤波的密集匹配方法。首先,利用特征匹配方法确定视差范围,并将零均值归一化互相关系数与图像灰度及梯度信息相结合构建代价计算函数;其次,利用超像素分割后的标签图来约束引导滤波窗口形状自适应变化,进行代价聚合;最后,将聚合代价作为数据项构建全局能量函数,用图割算法求解视差图,并对视差图作多步视差优化。实验结果表明,本文方法在Middlebury网站提供的标准测试图像集上平均误匹配率为4.8%,明显优于传统的引导滤波密集匹配方法与半全局匹配方法等。

关键词: 机器视觉, 密集匹配, 超像素分割, 引导滤波, 图割算法

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 was used to determine the disparity range, and the zero-mean normalized cross correlation was combined with image gray and gradient information to construct the cost calculation 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 4.84% 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.

Key words: Machine vision, dense matching, superpixel segmentation, guided filter, graph cut algorithm

中图分类号:

TP394.1

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, DOI: 10.7523/j.ucas.2023.081.

ZHANG Zheng, ZHANG Wenyi, XU Shu. Image dense matching algorithm combining superpixel segmentation and guided filtering[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2023.081.

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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

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