An Improved Stereo Matching Algorithm Based on Joint Similarity Measure and Adaptive Weights

被引：26
作者：
Lai, Xiangjun [1 ]
Yang, Bo [1 ]
Ma, Botao [1 ]
Liu, Mingzhe [1 ,2 ]
Yin, Zhengtong [1 ,3 ]
Yin, Lirong [4 ]
Zheng, Wenfeng [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Automat, Chengdu 610054, Peoples R China
[2] Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325000, Peoples R China
[3] Guizhou Univ, Coll Resource & Environm Engn, Guiyang 550025, Peoples R China
[4] Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA
来源：
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 01期
关键词：
joint similarity measure; binocular stereo vision; graph cutting; stereo matching; soft tissue; MOTION PREDICTION; SYSTEM;
D O I：
10.3390/app13010514
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Stereo matching is the operation of obtaining the parallax value between two images by matching all the corresponding image points in the two images, thus obtaining the dense parallax image between the two images. How to obtain accurate disparity images has always been a key point in the field of stereo vision. Presently, in the research of 3D reconstruction technology based on binocular stereo vision, the main research direction of domestic and foreign scholars is to improve the efficiency and accuracy of stereo matching, and there is research literature on soft tissues. This paper proposes an improved stereo matching algorithm based on joint similarity measures and adaptive weights. The algorithm improves the matching cost calculation based on the joint similarity measure to fit the color image of the heart soft tissue. At the same time, the algorithm uses the idea of graph cutting to improve the adaptive weight. The experimental results show that both the improved joint similarity measure and the improved adaptive weight can effectively reduce the mismatch rate. In addition, the corresponding matching effect is better than using only one of the improved joint similarity measures.
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页数：21
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