A novel image registration approach via combining local features and geometric invariants

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 dete...

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Bibliographic Details
Published in:PloS one 2018-01, Vol.13 (1), p.e0190383-e0190383
Main Authors: Lu, Yan, Gao, Kun, Zhang, Tinghua, Xu, Tingfa
Format: Article
Language:English
Subjects:
Accuracy
Adaptability
Algorithms
Computer and Information Sciences
Computer vision
Earth Sciences
Embedded systems
Engineering and Technology
Geometric constraints
Image processing
Image registration
Invariants
Matching
Medical imaging
Medicine and Health Sciences
Methods
NMR
Noise
Nuclear magnetic resonance
Pattern recognition
Physical Sciences
Registration
Research and Analysis Methods
Sensors
Studies
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0190383