Open AccessFeature matching using guidance‐constraint method
Author(s) -
Wang Zhiheng,
Wang Zhifei,
Liu Hongmin,
Huo Zhanqiang,
Fan Bin
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0354
Subject(s) - matching (statistics) , feature (linguistics) , constraint (computer aided design) , artificial intelligence , computer science , feature matching , pattern recognition (psychology) , image matching , image (mathematics) , feature extraction , computer vision , mathematics , statistics , philosophy , linguistics , geometry
Image distortions and repetitive patterns widely exist in real images, which results in that feature matching is still a challenging problem though great progress has been made recently. This study presents a matching method, called guidance‐constraints method (GCM), which has obvious advantages in resolving problems of matching features on images with image distortions or repetitive patterns. In GCM, feature points are paired and connection compatibility is introduced to describe the relative geometric relations among features, and then potential matches are found by using the defined geometric guidance and are verified by using the defined geometric constraints. Experimental evaluation shows that the proposed GCM can significantly improve both the number of correct matches and correct ratio under various image transformations, especially more effective on images with distortions or containing repetitive patterns.