Open AccessAn Improved Adaptive Window Stereo Matching Algorithm
Author(s) -
Wenbo Qiao,
Yuanping Xu,
Chaolong Zhang,
Zhijie Xu,
Jian Huang,
Pan Xie,
Jun Lu
Publication year - 2020
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 - 10.1088/1742-6596/1634/1/012066
Subject(s) - window (computing) , pixel , matching (statistics) , sobel operator , artificial intelligence , computer science , computer vision , blossom algorithm , feature (linguistics) , algorithm , pattern recognition (psychology) , template matching , image (mathematics) , mathematics , edge detection , image processing , linguistics , statistics , philosophy , operating system
In order to solve the problem that the existing adaptive window stereo matching algorithms have insufficient feature extraction in low-texture regions, resulting in low matching accuracy. An adaptive window stereo matching algorithm based on the gradient is proposed. Firstly, the Sobel operator is used to extract the gradient value of each pixel in the image. Then, each pixel is divided into high, medium and low texture regions according to the gradient value. Next, different arm length thresholds are assigned to different region pixels, and matching windows are generated dynamically according to arm length and color threshold. Finally, the pixels closer to the center of the window are given higher weights by generating windows several times. It solves the problem that the stereo matching algorithm can not select a matching window dynamically. Experimental results on Middlebury dataset show that the proposed method improves the matching accuracy by 5.5% compared with the latest adaptive window stereo matching algorithm.