Open AccessA novel image registration approach via combining local features and geometric invariants
Author(s) -
Yan Lu,
Kun Gao,
Tinghua Zhang,
Tingfa Xu
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0190383
Subject(s) - artificial intelligence , computer science , image registration , pattern recognition (psychology) , feature (linguistics) , matching (statistics) , point set registration , scale invariant feature transform , image (mathematics) , invariant (physics) , constraint (computer aided design) , feature detection (computer vision) , computer vision , image processing , mathematics , point (geometry) , philosophy , linguistics , statistics , geometry , mathematical physics
Image registration is widely used in many fields, but the adaptability of the existing methods is limited. This work proposes a novel image registration method with high precision for various complex applications. In this framework, the registration problem is divided into two stages. First, we detect and describe scale-invariant feature points using modified computer vision-oriented fast and rotated brief (ORB) algorithm, and a simple method to increase the performance of feature points matching is proposed. Second, we develop a new local constraint of rough selection according to the feature distances. Evidence shows that the existing matching techniques based on image features are insufficient for the images with sparse image details. Then, we propose a novel matching algorithm via geometric constraints, and establish local feature descriptions based on geometric invariances for the selected feature points. Subsequently, a new price function is constructed to evaluate the similarities between points and obtain exact matching pairs. Finally, we employ the progressive sample consensus method to remove wrong matches and calculate the space transform parameters. Experimental results on various complex image datasets verify that the proposed method is more robust and significantly reduces the rate of false matches while retaining more high-quality feature points.