Open AccessImage matching via progressive priors
Author(s) -
Wang Weiqing,
Sun Yongrong,
Zhao Kedong,
Liu Zhong,
Luo Wenjun,
Qin Jinchang
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12590
Subject(s) - prior probability , matching (statistics) , artificial intelligence , image (mathematics) , deformation (meteorology) , computer science , computer vision , pattern recognition (psychology) , image matching , mathematics , statistics , bayesian probability , geography , meteorology
It is a fundamental and challenging issue how to improve the accuracy of image matching in computer vision. To address this issue, an image matching method is proposed, which is via progressive priors of a putative dataset. Distance ratio antecedents of a presumptive dataset are initially employed to calculate a tentative deformation through geometric constraints. Progressive priors of the presumptive dataset, obtained by the tentative deformation, are then engaged to improve the accuracy of image matching by estimating a global deformation. The comparison experiments illustrate that our proposed method more effectively enhances the accuracy of image matching than six state‐of‐the‐art methods.