Automated detection and classification of galaxies based on their brightness patterns

Clues and traces of the universe's origin and its developmental process are deeply buried in galaxy shapes and formations. Automated galaxies classification from their images is complicated due to the faintness of the galaxy images, conflicting bright background stars, and image noise. For this...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohamed Eassa, Ibrahim Mohamed Selim, Walid Dabour, Passent Elkafrawy
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Galaxy classification Galaxy visual center Galaxy region detection K-means Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/fa5c38ad916f41e5962b1db3bc4ded0a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fa5c38ad916f41e5962b1db3bc4ded0a
record_format	dspace
spelling	oai:doaj.org-article:fa5c38ad916f41e5962b1db3bc4ded0a2021-11-18T04:45:14ZAutomated detection and classification of galaxies based on their brightness patterns1110-016810.1016/j.aej.2021.06.020https://doaj.org/article/fa5c38ad916f41e5962b1db3bc4ded0a2022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821003823https://doaj.org/toc/1110-0168Clues and traces of the universe's origin and its developmental process are deeply buried in galaxy shapes and formations. Automated galaxies classification from their images is complicated due to the faintness of the galaxy images, conflicting bright background stars, and image noise. For this purpose, the current work proposes a novel logically structured modular algorithm that analyses galaxy morphological raw brightness data to automatically detect galaxy visual center, region, and classification. First, a novel selective brightness threshold is employed to eliminate the effect of bright background stars on detecting galaxy visual centers. Second, a galaxy region detection technique is developed. Finally, a novel technique based on galaxy brightness variation patterns is employed for galaxies classification. The current work has been tested with a run on a collection of 1000 galaxies from the EFIGI catalog. Results demonstrated a success rate of 97.2% in galaxies classification with an average processing time of 0.37 s per galaxy. The high success rates and the low processing time proved the efficiency of the proposed work.Mohamed EassaIbrahim Mohamed SelimWalid DabourPassent ElkafrawyElsevierarticleGalaxy classificationGalaxy visual centerGalaxy region detectionK-meansEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1145-1158 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Galaxy classification Galaxy visual center Galaxy region detection K-means Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Galaxy classification Galaxy visual center Galaxy region detection K-means Engineering (General). Civil engineering (General) TA1-2040 Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour Passent Elkafrawy Automated detection and classification of galaxies based on their brightness patterns
description	Clues and traces of the universe's origin and its developmental process are deeply buried in galaxy shapes and formations. Automated galaxies classification from their images is complicated due to the faintness of the galaxy images, conflicting bright background stars, and image noise. For this purpose, the current work proposes a novel logically structured modular algorithm that analyses galaxy morphological raw brightness data to automatically detect galaxy visual center, region, and classification. First, a novel selective brightness threshold is employed to eliminate the effect of bright background stars on detecting galaxy visual centers. Second, a galaxy region detection technique is developed. Finally, a novel technique based on galaxy brightness variation patterns is employed for galaxies classification. The current work has been tested with a run on a collection of 1000 galaxies from the EFIGI catalog. Results demonstrated a success rate of 97.2% in galaxies classification with an average processing time of 0.37 s per galaxy. The high success rates and the low processing time proved the efficiency of the proposed work.
format	article
author	Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour Passent Elkafrawy
author_facet	Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour Passent Elkafrawy
author_sort	Mohamed Eassa
title	Automated detection and classification of galaxies based on their brightness patterns
title_short	Automated detection and classification of galaxies based on their brightness patterns
title_full	Automated detection and classification of galaxies based on their brightness patterns
title_fullStr	Automated detection and classification of galaxies based on their brightness patterns
title_full_unstemmed	Automated detection and classification of galaxies based on their brightness patterns
title_sort	automated detection and classification of galaxies based on their brightness patterns
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/fa5c38ad916f41e5962b1db3bc4ded0a
work_keys_str_mv	AT mohamedeassa automateddetectionandclassificationofgalaxiesbasedontheirbrightnesspatterns AT ibrahimmohamedselim automateddetectionandclassificationofgalaxiesbasedontheirbrightnesspatterns AT waliddabour automateddetectionandclassificationofgalaxiesbasedontheirbrightnesspatterns AT passentelkafrawy automateddetectionandclassificationofgalaxiesbasedontheirbrightnesspatterns
_version_	1718425077396013056

Automated detection and classification of galaxies based on their brightness patterns

Ejemplares similares