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dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorPereira, Clayton R. [UNESP]
dc.contributor.authorDe Albuquerque, Victor H. C.
dc.contributor.authorSilva, Cleiton C.
dc.contributor.authorFalcão, Alexandre X.
dc.contributor.authorTavares, João Manuel R. S.
dc.date.accessioned2014-05-27T11:25:54Z
dc.date.available2014-05-27T11:25:54Z
dc.date.issued2011-06-02
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-21073-0_40
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/11449/72488
dc.description.abstractThe 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.en
dc.format.extent456-468
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.subjectHastelloy C-276
dc.subjectMetallic Precipitates Segmentation
dc.subjectOptimum-Path Forest
dc.subjectScanning Electron Microscope
dc.subjectSupport Vector Machines
dc.subjectAutomatic identification
dc.subjectBayesian classifier
dc.subjectDissimilar welding
dc.subjectMachine learning techniques
dc.subjectMetallic material
dc.subjectMetallographic images
dc.subjectRecognition rates
dc.subjectSupervised pattern recognition
dc.subjectAutomation
dc.subjectDurability
dc.subjectElectron microscopes
dc.subjectImage analysis
dc.subjectLearning algorithms
dc.subjectPattern recognition
dc.subjectScanning
dc.subjectScanning electron microscopy
dc.subjectSelf organizing maps
dc.subjectSupport vector machines
dc.subjectImage segmentation
dc.titlePrecipitates segmentation from scanning electron microscope images through machine learning techniquesen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversity of Fortaleza
dc.contributor.institutionFederal University of Ceará
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversity of Porto
dc.description.affiliationDep. of Computing UNESP Univ Estadual Paulista, Bauru
dc.description.affiliationCenter of Technological Sciences University of Fortaleza, Fortaleza
dc.description.affiliationDep. of Materials and Metallurgical Engineering Federal University of Ceará
dc.description.affiliationInstitute of Computing State University of Campinas, Campinas
dc.description.affiliationFaculty of Engineering University of Porto, Porto
dc.description.affiliationUnespDep. of Computing UNESP Univ Estadual Paulista, Bauru
dc.identifier.doi10.1007/978-3-642-21073-0_40
dc.identifier.wosWOS:000303500200040
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-79957648069
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Ciências, Baurupt
dc.identifier.lattes9039182932747194
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0001-7603-6526[6]
unesp.author.orcid0000-0002-6494-7514[1]
unesp.author.orcid0000-0001-6827-8939[4]
unesp.author.orcid0000-0003-3886-4309[3]
dc.relation.ispartofsjr0,295
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