Oriented texture classification based on self-organizing neural network and Hough transform
Autor(a) principal: | |
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Data de Publicação: | 1997 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ICASSP.1997.595364 http://hdl.handle.net/11449/64992 |
Resumo: | This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters. |
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Repositório Institucional da UNESP |
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Oriented texture classification based on self-organizing neural network and Hough transformMathematical transformationsNeural networksHough transformKohonen's self organizing mapOriented texture classificationFeature extractionThis paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.Unesp, Sao PauloUnesp, Sao PauloUniversidade Estadual Paulista (Unesp)Marana, Aparecido Nilceu [UNESP]da, L. [UNESP]Velastin, S. A. [UNESP]Lotufo, R. A. [UNESP]2014-05-27T11:18:10Z2014-05-27T11:18:10Z1997-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2773-2775http://dx.doi.org/10.1109/ICASSP.1997.595364ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 4, p. 2773-2775.0736-7791http://hdl.handle.net/11449/6499210.1109/ICASSP.1997.5953642-s2.0-00307014246027713750942689Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings0,402info:eu-repo/semantics/openAccess2024-04-23T16:11:27Zoai:repositorio.unesp.br:11449/64992Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:49:11.188211Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Oriented texture classification based on self-organizing neural network and Hough transform |
title |
Oriented texture classification based on self-organizing neural network and Hough transform |
spellingShingle |
Oriented texture classification based on self-organizing neural network and Hough transform Marana, Aparecido Nilceu [UNESP] Mathematical transformations Neural networks Hough transform Kohonen's self organizing map Oriented texture classification Feature extraction |
title_short |
Oriented texture classification based on self-organizing neural network and Hough transform |
title_full |
Oriented texture classification based on self-organizing neural network and Hough transform |
title_fullStr |
Oriented texture classification based on self-organizing neural network and Hough transform |
title_full_unstemmed |
Oriented texture classification based on self-organizing neural network and Hough transform |
title_sort |
Oriented texture classification based on self-organizing neural network and Hough transform |
author |
Marana, Aparecido Nilceu [UNESP] |
author_facet |
Marana, Aparecido Nilceu [UNESP] da, L. [UNESP] Velastin, S. A. [UNESP] Lotufo, R. A. [UNESP] |
author_role |
author |
author2 |
da, L. [UNESP] Velastin, S. A. [UNESP] Lotufo, R. A. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Marana, Aparecido Nilceu [UNESP] da, L. [UNESP] Velastin, S. A. [UNESP] Lotufo, R. A. [UNESP] |
dc.subject.por.fl_str_mv |
Mathematical transformations Neural networks Hough transform Kohonen's self organizing map Oriented texture classification Feature extraction |
topic |
Mathematical transformations Neural networks Hough transform Kohonen's self organizing map Oriented texture classification Feature extraction |
description |
This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters. |
publishDate |
1997 |
dc.date.none.fl_str_mv |
1997-01-01 2014-05-27T11:18:10Z 2014-05-27T11:18:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ICASSP.1997.595364 ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 4, p. 2773-2775. 0736-7791 http://hdl.handle.net/11449/64992 10.1109/ICASSP.1997.595364 2-s2.0-0030701424 6027713750942689 |
url |
http://dx.doi.org/10.1109/ICASSP.1997.595364 http://hdl.handle.net/11449/64992 |
identifier_str_mv |
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 4, p. 2773-2775. 0736-7791 10.1109/ICASSP.1997.595364 2-s2.0-0030701424 6027713750942689 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 0,402 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2773-2775 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129252823924736 |