Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

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dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorDe Albuquerque, Victor Hugo C.
dc.contributor.authorFalcão, Alexandre Xavier
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionTechnological Research Center
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionFaculty of Engineering
dc.date.accessioned2014-05-27T11:24:41Z
dc.date.available2014-05-27T11:24:41Z
dc.date.issued2010-05-21
dc.description.abstractIn this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.en
dc.description.affiliationSão Paulo State University Computer Science Department, Bauru
dc.description.affiliationUniversity of Fortaleza Technological Research Center, Fortaleza
dc.description.affiliationUniversity of Campinas Institute of Computing, Campinas
dc.description.affiliationUniversity of Porto Faculty of Engineering, Porto
dc.description.affiliationUnespSão Paulo State University Computer Science Department, Bauru
dc.format.extent210-220
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-12712-0_19
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6026 LNCS, p. 210-220.
dc.identifier.doi10.1007/978-3-642-12712-0_19
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.lattes9039182932747194
dc.identifier.scopus2-s2.0-77952364349
dc.identifier.urihttp://hdl.handle.net/11449/71689
dc.identifier.wosWOS:000279020400019
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCast irons
dc.subjectImage segmentation
dc.subjectMaterials science
dc.subjectMicrostructural evaluation
dc.subjectSupervised classification
dc.subjectFerrous alloys
dc.subjectForest classifiers
dc.subjectKernel mapping
dc.subjectMalleable cast iron
dc.subjectMicro-structural
dc.subjectMicroscopic image
dc.subjectRadial basis functions
dc.subjectSegmented images
dc.subjectDamping
dc.subjectDigital image storage
dc.subjectIron
dc.subjectMalleable iron castings
dc.subjectRadial basis function networks
dc.subjectSupport vector machines
dc.subjectCast iron
dc.titleFast automatic microstructural segmentation of ferrous alloy samples using optimum-path foresten
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0002-6494-7514[1]
unesp.author.orcid0000-0003-3886-4309[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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