Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3 |
Resumo: | Entropy based image thresholding methods are widely adopted for multilevel image segmentation. Bilevel thresholding partitions an image into two classes, whereas multilevel thresholding partitions an image into multiple classes depending upon thresholding level . The automatic selection of optimal threshold is often treated as an optimization problem. This paper contributes to novel thresholding method, that is based on entropy of fuzzy c partition and gravitational search algorithm (GSA). Experiments have been evaluated on the different test images and results were assessed by entropy, stability, computation time and peak signal to noise ratio (PSNR). The analysis of results conveys that the GSA outperform particle swarm optimization (PSO). |
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INFOCOMP: Jornal de Ciência da Computação |
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Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search AlgorithmEntropy based image thresholding methods are widely adopted for multilevel image segmentation. Bilevel thresholding partitions an image into two classes, whereas multilevel thresholding partitions an image into multiple classes depending upon thresholding level . The automatic selection of optimal threshold is often treated as an optimization problem. This paper contributes to novel thresholding method, that is based on entropy of fuzzy c partition and gravitational search algorithm (GSA). Experiments have been evaluated on the different test images and results were assessed by entropy, stability, computation time and peak signal to noise ratio (PSNR). The analysis of results conveys that the GSA outperform particle swarm optimization (PSO).Editora da UFLA2014-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3INFOCOMP Journal of Computer Science; Vol. 13 No. 1 (2014): June 2014; 1-111982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3/3Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessGupta, ChhaviJain, Sanjeev2015-07-29T16:47:19Zoai:infocomp.dcc.ufla.br:article/3Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:11.588152INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
title |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
spellingShingle |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm Gupta, Chhavi |
title_short |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
title_full |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
title_fullStr |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
title_full_unstemmed |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
title_sort |
Multilevel Thresholding based on Fuzzy C Partition and Gravitational Search Algorithm |
author |
Gupta, Chhavi |
author_facet |
Gupta, Chhavi Jain, Sanjeev |
author_role |
author |
author2 |
Jain, Sanjeev |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Gupta, Chhavi Jain, Sanjeev |
description |
Entropy based image thresholding methods are widely adopted for multilevel image segmentation. Bilevel thresholding partitions an image into two classes, whereas multilevel thresholding partitions an image into multiple classes depending upon thresholding level . The automatic selection of optimal threshold is often treated as an optimization problem. This paper contributes to novel thresholding method, that is based on entropy of fuzzy c partition and gravitational search algorithm (GSA). Experiments have been evaluated on the different test images and results were assessed by entropy, stability, computation time and peak signal to noise ratio (PSNR). The analysis of results conveys that the GSA outperform particle swarm optimization (PSO). |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3/3 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 13 No. 1 (2014): June 2014; 1-11 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874739960807424 |