<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.</p>
</abstract>

Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Communication Networks, Architecture and Multimedia (RCAM) Laboratory, Faculty of Electrical Engineering

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Rigid Image Registration by Combined Local Features and Genetic Algorithms"}

Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is...

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Pubblicato online: 29 gen 2024

Pagine: 252 - 257

DOI: https://doi.org/10.2478/acss-2023-0025

Parole chiave
Combined local feature points, genetic algorithms, image registration, rigid transformation

© 2023 Fatiha Meskine et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Pubblicato online: 29 gen 2024

Pagine: 252 - 257

DOI: https://doi.org/10.2478/acss-2023-0025

Parole chiaveCombined local feature points, genetic algorithms, image registration, rigid transformation

© 2023 Fatiha Meskine et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Combined local feature points, genetic algorithms, image registration, rigid transformation