Precipitates segmentation from scanning electron microscope images through machine learning techniques

Detalhes bibliográficos
Autor(a) principal: Papa, João Paulo [UNESP]
Data de Publicação: 2011
Outros Autores: Pereira, Clayton R. [UNESP], De Albuquerque, Victor H. C., Silva, Cleiton C., Falcão, Alexandre X., Tavares, João Manuel R. S.
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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spelling 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/openAccess2021-10-23T21:44:11Zoai:repositorio.unesp.br:11449/72488Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:11Repositório 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
_version_ 1792962181586747392

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