Open AccessResearch on Image Feature Point Matching Based on ORB and RANSAC Algorithm
Author(s) -
Hua Zhang,
Zheng Guo-xun,
Haohai Fu
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 - 10.1088/1742-6596/1651/1/012187
Subject(s) - ransac , orb (optics) , artificial intelligence , matching (statistics) , computer vision , feature (linguistics) , scale invariant feature transform , computer science , feature detection (computer vision) , filter (signal processing) , point set registration , homography , blossom algorithm , rotation (mathematics) , pattern recognition (psychology) , image (mathematics) , algorithm , template matching , point (geometry) , image processing , mathematics , linguistics , statistics , philosophy , geometry , projective test , projective space
Image matching is one of the basic problems in the field of machine vision research. In order to improve the accuracy of image feature point matching and enhance the anti-interference ability of the algorithm, for the shortcomings of traditional ORB algorithm, an image feature point matching algorithm based on ORB and RANSAC algorithm is proposed. The algorithm can filter out false matches on the basis of coarse matching results, and filter out exact matching points through the homography matrix. Experiments show that this method can effectively improve the accuracy of image matching and shorten the execution time, and it is very robust to image matching with different scales, ambiguities, brightness and rotation.