Open AccessFeature Point Matching Based on Local Relative Velocity Consensus
Author(s) -
Feng Shao,
ZhaoXia Liu,
Jubai
Publication year - 2021
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 - 10.1088/1742-6596/1871/1/012055
Subject(s) - outlier , robustness (evolution) , matching (statistics) , feature (linguistics) , pattern recognition (psychology) , feature matching , artificial intelligence , computer science , feature extraction , point set registration , similarity (geometry) , mathematics , point (geometry) , image (mathematics) , statistics , biochemistry , chemistry , linguistics , philosophy , geometry , gene
Feature matching is still a crucial and challenging problem in image processing. In this paper, a novel feature matching algorithm based on local relative velocity consensus (LRVC) is proposed to find two accurate matched feature points. To remove the outliers accurately and robustly, the relative velocity between the putative matches and their corresponding neighbors are exploited. Based on the consensus of relative velocity and the consensus of neighborhood elements, the similarity of each putative matches is evaluated. With a two iteration outlier removal strategy, the feature points are matched accurately and robustly. In the experiment, VGG dataset are used to verify the accuracy and robustness of the proposed method LRVC. The matching results indicate that our method is superior to three other classic feature matching methods.