Image registration based on genetic algorithm and weighted feature correspondences

Zhi Yuan; Alireza Ahrary; Peimin Yan; Sei Ichiro Kamata

doi:10.1109/ISCE.2009.5157002

Image registration based on genetic algorithm and weighted feature correspondences

Zhi Yuan^*, Alireza Ahrary, Peimin Yan, Sei Ichiro Kamata

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Super resolution is a technique of enhancing image resolution by combining information from multiple images. It is widely applied in fields like camera surveillance, satellite imaging, pattern recognition, etc. One challenging problem of super resolution is its high demand on image registration accuracy. This paper introduces a high accuracy registration approach for the purpose of super resolution. It is invariant to translation, scaling, rotation, and noise, and can be used to automatically obtain the Maximize a Likelihood Estimation (MLE) of image homography (registration result) using information only contained within the images themselves. An effective Genetic Algorithm based approach is used to filter out all the mismatches. Comparison with RANSAC and Keren's method will be given to prove the effectiveness of the proposed method.

Original language	English
Title of host publication	2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009
Pages	42-46
Number of pages	5
DOIs	https://doi.org/10.1109/ISCE.2009.5157002
Publication status	Published - 2009
Event	2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009 - Kyoto, Japan Duration: 2009 May 25 → 2009 May 28

Publication series

Name	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)	0747-668X

Conference

Conference	2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009
Country/Territory	Japan
City	Kyoto
Period	09/5/25 → 09/5/28

Keywords

Backward projection
Genetic Algorithm
Image registration
RANSAC (Random Sampling Consensus)
SIFT (Scale Invairant Feature Transform)
Super resolution

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/ISCE.2009.5157002

Cite this

Yuan, Z., Ahrary, A., Yan, P., & Kamata, S. I. (2009). Image registration based on genetic algorithm and weighted feature correspondences. In 2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009 (pp. 42-46). Article 5157002 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics). https://doi.org/10.1109/ISCE.2009.5157002

Image registration based on genetic algorithm and weighted feature correspondences. / Yuan, Zhi; Ahrary, Alireza; Yan, Peimin et al.
2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009. 2009. p. 42-46 5157002 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yuan, Z, Ahrary, A, Yan, P & Kamata, SI 2009, Image registration based on genetic algorithm and weighted feature correspondences. in 2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009., 5157002, Digest of Technical Papers - IEEE International Conference on Consumer Electronics, pp. 42-46, 2009 IEEE 13th International Symposium on Consumer Electronics, ISCE 2009, Kyoto, Japan, 09/5/25. https://doi.org/10.1109/ISCE.2009.5157002

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title = "Image registration based on genetic algorithm and weighted feature correspondences",

abstract = "Super resolution is a technique of enhancing image resolution by combining information from multiple images. It is widely applied in fields like camera surveillance, satellite imaging, pattern recognition, etc. One challenging problem of super resolution is its high demand on image registration accuracy. This paper introduces a high accuracy registration approach for the purpose of super resolution. It is invariant to translation, scaling, rotation, and noise, and can be used to automatically obtain the Maximize a Likelihood Estimation (MLE) of image homography (registration result) using information only contained within the images themselves. An effective Genetic Algorithm based approach is used to filter out all the mismatches. Comparison with RANSAC and Keren's method will be given to prove the effectiveness of the proposed method.",

keywords = "Backward projection, Genetic Algorithm, Image registration, RANSAC (Random Sampling Consensus), SIFT (Scale Invairant Feature Transform), Super resolution",

author = "Zhi Yuan and Alireza Ahrary and Peimin Yan and Kamata, {Sei Ichiro}",

year = "2009",

doi = "10.1109/ISCE.2009.5157002",

language = "English",

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series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",

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AU - Yan, Peimin

AU - Kamata, Sei Ichiro

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N2 - Super resolution is a technique of enhancing image resolution by combining information from multiple images. It is widely applied in fields like camera surveillance, satellite imaging, pattern recognition, etc. One challenging problem of super resolution is its high demand on image registration accuracy. This paper introduces a high accuracy registration approach for the purpose of super resolution. It is invariant to translation, scaling, rotation, and noise, and can be used to automatically obtain the Maximize a Likelihood Estimation (MLE) of image homography (registration result) using information only contained within the images themselves. An effective Genetic Algorithm based approach is used to filter out all the mismatches. Comparison with RANSAC and Keren's method will be given to prove the effectiveness of the proposed method.

AB - Super resolution is a technique of enhancing image resolution by combining information from multiple images. It is widely applied in fields like camera surveillance, satellite imaging, pattern recognition, etc. One challenging problem of super resolution is its high demand on image registration accuracy. This paper introduces a high accuracy registration approach for the purpose of super resolution. It is invariant to translation, scaling, rotation, and noise, and can be used to automatically obtain the Maximize a Likelihood Estimation (MLE) of image homography (registration result) using information only contained within the images themselves. An effective Genetic Algorithm based approach is used to filter out all the mismatches. Comparison with RANSAC and Keren's method will be given to prove the effectiveness of the proposed method.

KW - Backward projection

KW - Genetic Algorithm

KW - Image registration

KW - RANSAC (Random Sampling Consensus)

KW - SIFT (Scale Invairant Feature Transform)

KW - Super resolution

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Image registration based on genetic algorithm and weighted feature correspondences

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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