Precipitates segmentation from scanning electron microscope images through machine learning techniques
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-642-21073-0_40 http://hdl.handle.net/11449/72488 |
Resumo: | The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg. |
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Precipitates segmentation from scanning electron microscope images through machine learning techniquesHastelloy C-276Metallic Precipitates SegmentationOptimum-Path ForestScanning Electron MicroscopeSupport Vector MachinesAutomatic identificationBayesian classifierDissimilar weldingMachine learning techniquesMetallic materialMetallographic imagesRecognition ratesSupervised pattern recognitionAutomationDurabilityElectron microscopesImage analysisLearning algorithmsPattern recognitionScanningScanning electron microscopySelf organizing mapsSupport vector machinesImage segmentationThe presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.Dep. of Computing UNESP Univ Estadual Paulista, BauruCenter of Technological Sciences University of Fortaleza, FortalezaDep. of Materials and Metallurgical Engineering Federal University of CearáInstitute of Computing State University of Campinas, CampinasFaculty of Engineering University of Porto, PortoDep. of Computing UNESP Univ Estadual Paulista, BauruUniversidade Estadual Paulista (Unesp)University of FortalezaFederal University of CearáUniversidade Estadual de Campinas (UNICAMP)University of PortoPapa, João Paulo [UNESP]Pereira, Clayton R. [UNESP]De Albuquerque, Victor H. C.Silva, Cleiton C.Falcão, Alexandre X.Tavares, João Manuel R. S.2014-05-27T11:25:54Z2014-05-27T11:25:54Z2011-06-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject456-468http://dx.doi.org/10.1007/978-3-642-21073-0_40Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468.0302-97431611-3349http://hdl.handle.net/11449/7248810.1007/978-3-642-21073-0_40WOS:0003035002000402-s2.0-799576480699039182932747194Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2024-04-23T16:11:28Zoai:repositorio.unesp.br:11449/72488Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:28Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
title |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
spellingShingle |
Precipitates segmentation from scanning electron microscope images through machine learning techniques Papa, João Paulo [UNESP] Hastelloy C-276 Metallic Precipitates Segmentation Optimum-Path Forest Scanning Electron Microscope Support Vector Machines Automatic identification Bayesian classifier Dissimilar welding Machine learning techniques Metallic material Metallographic images Recognition rates Supervised pattern recognition Automation Durability Electron microscopes Image analysis Learning algorithms Pattern recognition Scanning Scanning electron microscopy Self organizing maps Support vector machines Image segmentation |
title_short |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
title_full |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
title_fullStr |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
title_full_unstemmed |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
title_sort |
Precipitates segmentation from scanning electron microscope images through machine learning techniques |
author |
Papa, João Paulo [UNESP] |
author_facet |
Papa, João Paulo [UNESP] Pereira, Clayton R. [UNESP] De Albuquerque, Victor H. C. Silva, Cleiton C. Falcão, Alexandre X. Tavares, João Manuel R. S. |
author_role |
author |
author2 |
Pereira, Clayton R. [UNESP] De Albuquerque, Victor H. C. Silva, Cleiton C. Falcão, Alexandre X. Tavares, João Manuel R. S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Fortaleza Federal University of Ceará Universidade Estadual de Campinas (UNICAMP) University of Porto |
dc.contributor.author.fl_str_mv |
Papa, João Paulo [UNESP] Pereira, Clayton R. [UNESP] De Albuquerque, Victor H. C. Silva, Cleiton C. Falcão, Alexandre X. Tavares, João Manuel R. S. |
dc.subject.por.fl_str_mv |
Hastelloy C-276 Metallic Precipitates Segmentation Optimum-Path Forest Scanning Electron Microscope Support Vector Machines Automatic identification Bayesian classifier Dissimilar welding Machine learning techniques Metallic material Metallographic images Recognition rates Supervised pattern recognition Automation Durability Electron microscopes Image analysis Learning algorithms Pattern recognition Scanning Scanning electron microscopy Self organizing maps Support vector machines Image segmentation |
topic |
Hastelloy C-276 Metallic Precipitates Segmentation Optimum-Path Forest Scanning Electron Microscope Support Vector Machines Automatic identification Bayesian classifier Dissimilar welding Machine learning techniques Metallic material Metallographic images Recognition rates Supervised pattern recognition Automation Durability Electron microscopes Image analysis Learning algorithms Pattern recognition Scanning Scanning electron microscopy Self organizing maps Support vector machines Image segmentation |
description |
The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-06-02 2014-05-27T11:25:54Z 2014-05-27T11:25:54Z |
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/10.1007/978-3-642-21073-0_40 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468. 0302-9743 1611-3349 http://hdl.handle.net/11449/72488 10.1007/978-3-642-21073-0_40 WOS:000303500200040 2-s2.0-79957648069 9039182932747194 |
url |
http://dx.doi.org/10.1007/978-3-642-21073-0_40 http://hdl.handle.net/11449/72488 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468. 0302-9743 1611-3349 10.1007/978-3-642-21073-0_40 WOS:000303500200040 2-s2.0-79957648069 9039182932747194 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
456-468 |
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 |
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1797790117554814976 |