Open AccessImprovement of stereo matching algorithm based on guided filtering and Kernel Regression
Author(s) -
Ying Cao,
Jin Gen Liu,
Tie Xiang Wen,
Benlian Xu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1213/3/032014
Subject(s) - benchmark (surveying) , artificial intelligence , kernel (algebra) , matching (statistics) , enhanced data rates for gsm evolution , computer science , computer vision , regression , interpolation (computer graphics) , algorithm , stereopsis , image (mathematics) , pattern recognition (psychology) , mathematics , geography , statistics , geodesy , combinatorics
Stereo matching is one of the most dynamic fields in computer vision. Though its relevant research has already stepped into a mature stage, there are still certain challenges to obtain real-time and high-precision disparity maps from stereo image pairs. This paper presents a novel local stereo matching algorithm with better performance in edge preserving. In the first stage, this paper measures matching cost through combining truncated absolute differences (TAD) of the color and gradient. In the cost aggregation stage, this paper is creatively to combined the weighted guided filtering and adaptive steering kernel regression algorithm, which effectively preserves image edge and depth information. In the final stage, an adaptive steering kernel regression algorithm is employed in interpolation to refine the final disparity map. According to the Middlebury benchmark experiments, the algorithm proposed in this paper could have better performance than other local stereo matching algorithms.