Cluster-Based Memetic Approach of Image Alignment

The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels belonging to the target image and to the sensed i...

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Autores principales:	Catalina-Lucia Cocianu, Cristian Răzvan Uscatu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bio-inspired computing evolutionary strategies firefly algorithm meta-heuristics rigid transformation image registration Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/4d12a8bb826e408ab65ce40ca06e07fe
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id	oai:doaj.org-article:4d12a8bb826e408ab65ce40ca06e07fe
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spelling	oai:doaj.org-article:4d12a8bb826e408ab65ce40ca06e07fe2021-11-11T15:37:50ZCluster-Based Memetic Approach of Image Alignment10.3390/electronics102126062079-9292https://doaj.org/article/4d12a8bb826e408ab65ce40ca06e07fe2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2606https://doaj.org/toc/2079-9292The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels belonging to the target image and to the sensed image respectively, the boundaries of the search space are computed. Next, the registration mechanism residing in a hybridization between a version of firefly population-based search procedure and the two membered evolutionary strategy computed on clustered data is applied. In addition, a procedure designed to deal with the premature convergence problem is embedded. The fitness to be maximized by the memetic algorithm is defined by the Dice coefficient, a function implemented to evaluate the similarity between pairs of binary images. The proposed methodology is applied on both binary and monochrome images. In case of monochrome images, a preprocessing step aiming the binarization of the inputs is considered before the registration. The quality of the proposed approach is measured in terms of accuracy and efficiency. The success rate based on Dice coefficient, normalized mutual information measures, and signal-to-noise ratio are used to establish the accuracy of the obtained algorithm, while the efficiency is evaluated by the run time function.Catalina-Lucia CocianuCristian Răzvan UscatuMDPI AGarticlebio-inspired computingevolutionary strategiesfirefly algorithmmeta-heuristicsrigid transformationimage registrationElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2606, p 2606 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bio-inspired computing evolutionary strategies firefly algorithm meta-heuristics rigid transformation image registration Electronics TK7800-8360
spellingShingle	bio-inspired computing evolutionary strategies firefly algorithm meta-heuristics rigid transformation image registration Electronics TK7800-8360 Catalina-Lucia Cocianu Cristian Răzvan Uscatu Cluster-Based Memetic Approach of Image Alignment
description	The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels belonging to the target image and to the sensed image respectively, the boundaries of the search space are computed. Next, the registration mechanism residing in a hybridization between a version of firefly population-based search procedure and the two membered evolutionary strategy computed on clustered data is applied. In addition, a procedure designed to deal with the premature convergence problem is embedded. The fitness to be maximized by the memetic algorithm is defined by the Dice coefficient, a function implemented to evaluate the similarity between pairs of binary images. The proposed methodology is applied on both binary and monochrome images. In case of monochrome images, a preprocessing step aiming the binarization of the inputs is considered before the registration. The quality of the proposed approach is measured in terms of accuracy and efficiency. The success rate based on Dice coefficient, normalized mutual information measures, and signal-to-noise ratio are used to establish the accuracy of the obtained algorithm, while the efficiency is evaluated by the run time function.
format	article
author	Catalina-Lucia Cocianu Cristian Răzvan Uscatu
author_facet	Catalina-Lucia Cocianu Cristian Răzvan Uscatu
author_sort	Catalina-Lucia Cocianu
title	Cluster-Based Memetic Approach of Image Alignment
title_short	Cluster-Based Memetic Approach of Image Alignment
title_full	Cluster-Based Memetic Approach of Image Alignment
title_fullStr	Cluster-Based Memetic Approach of Image Alignment
title_full_unstemmed	Cluster-Based Memetic Approach of Image Alignment
title_sort	cluster-based memetic approach of image alignment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4d12a8bb826e408ab65ce40ca06e07fe
work_keys_str_mv	AT catalinaluciacocianu clusterbasedmemeticapproachofimagealignment AT cristianrazvanuscatu clusterbasedmemeticapproachofimagealignment
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Cluster-Based Memetic Approach of Image Alignment

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