An Improved Stereo Matching Algorithm Based on Joint Similarity Measure and Adaptive Weights
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Vision and Imaging · Image Enhancement Techniques · Advanced Technologies in Various Fields
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China
College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Funding
Sichuan Science and Technology Program
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