Journal of Chaohu University ›› 2020, Vol. 22 ›› Issue (6): 77-85.doi: 10.12152/j.issn.1672-2868.2020.06.011

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An Improved Image Matching Algorithm for ORB Feature Descriptors

WANG Yong-sheng,LI Yan,LIU Ming:School of Mathematics and Computing, Tongling University   

  1. School of Mathematics and Computing, Tongling University, Tongling Anhui 244061
  • Received:2020-06-29 Online:2020-11-25 Published:2021-02-02

Abstract: The image matching effect of ORB algorithm largely depends on the quality of feature point descriptors. Aiming at the disadvantages of traditional ORB feature descriptors, such as poor stability and weak noise resistance, an improved image matching algorithm for ORB feature descriptors is proposed. The improved dynamic adaptive threshold FAST algorithm is used to detect and extract image feature points, and the improved ORB feature descriptor is used to describe the matching feature points. The rough feature points of matching images are obtained by brute force matching method, and the incorrect matching feature points are eliminated by RANSAC algorithm to sift out the exact matching points. Compared with random descriptors and sample training descriptors, the experiment shows that the improved algorithm can effectively improve the quality of image matching, and it is better in the number and accuracy of matching feature points, which verifies the effectiveness of the improved algorithm.

Key words: feature matching, ORB, FAST, feature descriptor

CLC Number: 

  • TP391.4