Open AccessAdvanced weight graph transformation matching algorithm
Author(s) -
Wang Song,
Guo Xin,
Mu Xiaomin,
Huo Yahong,
Qi Lin
Publication year - 2015
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.2014.0339
Subject(s) - matching (statistics) , outlier , transformation (genetics) , blossom algorithm , graph , point set registration , algorithm , 3 dimensional matching , pattern recognition (psychology) , computer science , mathematics , artificial intelligence , point (geometry) , theoretical computer science , statistics , biochemistry , chemistry , geometry , gene
An efficient and accurate point matching algorithm named advanced weight graph transformation matching (AWGTM) is proposed in this study. Instead of relying only on the elimination of dubious matches, the method iteratively reserve correspondences which have a small angular distance between two nearest‐neighbour graphs. The proposed algorithm is compared against weight graph transformation matching (WGTM) and graph transformation matching (GTM). Experimental results demonstrate the superior performance in eliminating outliers and reserving inliers of AWGTM algorithm under various conditions for images, such as duplication of patterns and non‐rigid deformation of objects. An execution time comparison is also presented, where AWGTM shows the best results for high outlier rates.