A wavelet-based approach for analyzing industrial stochastic textures with applications
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/27605 |
Resumo: | Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work. |
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Scharcanski, Jacob2011-01-29T06:00:38Z20071083-4427http://hdl.handle.net/10183/27605000615536Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work.application/pdfengIEEE transactions on systems, man, and cybernetics. Part A : Systems and humans. Vol. 37, no. 1 (Jan. 2007), p. 10-22Automação industrialReconhecimento : PadroesAnisotropyColored noiseIndustrial quality controlMaintenanceNonwoven textilesStochastic texturesWaveletsA wavelet-based approach for analyzing industrial stochastic textures with applicationsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000615536.pdf000615536.pdfTexto completo (inglês)application/pdf956645http://www.lume.ufrgs.br/bitstream/10183/27605/1/000615536.pdf82bc3c02e92166f469e1a3972901d227MD51TEXT000615536.pdf.txt000615536.pdf.txtExtracted Texttext/plain57036http://www.lume.ufrgs.br/bitstream/10183/27605/2/000615536.pdf.txt71fd9e87cb4ad36a2458509776d02515MD52THUMBNAIL000615536.pdf.jpg000615536.pdf.jpgGenerated Thumbnailimage/jpeg2305http://www.lume.ufrgs.br/bitstream/10183/27605/3/000615536.pdf.jpgea4b8edca3a02d07e842d7c8f5b01f89MD5310183/276052021-06-13 04:32:23.862056oai:www.lume.ufrgs.br:10183/27605Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-06-13T07:32:23Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
title |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
spellingShingle |
A wavelet-based approach for analyzing industrial stochastic textures with applications Scharcanski, Jacob Automação industrial Reconhecimento : Padroes Anisotropy Colored noise Industrial quality control Maintenance Nonwoven textiles Stochastic textures Wavelets |
title_short |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
title_full |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
title_fullStr |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
title_full_unstemmed |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
title_sort |
A wavelet-based approach for analyzing industrial stochastic textures with applications |
author |
Scharcanski, Jacob |
author_facet |
Scharcanski, Jacob |
author_role |
author |
dc.contributor.author.fl_str_mv |
Scharcanski, Jacob |
dc.subject.por.fl_str_mv |
Automação industrial Reconhecimento : Padroes |
topic |
Automação industrial Reconhecimento : Padroes Anisotropy Colored noise Industrial quality control Maintenance Nonwoven textiles Stochastic textures Wavelets |
dc.subject.eng.fl_str_mv |
Anisotropy Colored noise Industrial quality control Maintenance Nonwoven textiles Stochastic textures Wavelets |
description |
Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing important information about both the materials produced and the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among industrial stochastic images or to discriminate between texture images of stochastic materials with distinct properties. Nowadays, the degree of discrimination required by industrial processes often goes beyond the limits of human visual perception. This paper proposes to model this specific class of textures as colored noise and presents a new approach for multiresolution stochastic texture representation and discrimination in industry (e.g., nonwoven textiles and paper). The wavelet transform is used to represent stochastic texture images in multiple resolutions and to describe them using local orientation and density variability as features. Based on this representation, a multiresolution distance measure for stochastic textures is proposed, and industrial applications of the method and experimental results are reported. The conclusions include ideas for future work. |
publishDate |
2007 |
dc.date.issued.fl_str_mv |
2007 |
dc.date.accessioned.fl_str_mv |
2011-01-29T06:00:38Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/27605 |
dc.identifier.issn.pt_BR.fl_str_mv |
1083-4427 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000615536 |
identifier_str_mv |
1083-4427 000615536 |
url |
http://hdl.handle.net/10183/27605 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
IEEE transactions on systems, man, and cybernetics. Part A : Systems and humans. Vol. 37, no. 1 (Jan. 2007), p. 10-22 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Repositório Institucional da UFRGS |
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