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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/71689
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dc.contributor.authorPapa, João Paulo-
dc.contributor.authorDe Albuquerque, Victor Hugo C.-
dc.contributor.authorFalcão, Alexandre Xavier-
dc.contributor.authorTavares, João Manuel R. S.-
dc.date.accessioned2014-05-27T11:24:41Z-
dc.date.accessioned2016-10-25T18:28:39Z-
dc.date.available2014-05-27T11:24:41Z-
dc.date.available2016-10-25T18:28:39Z-
dc.date.issued2010-05-21-
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.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttp://hdl.handle.net/11449/71689-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/71689-
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.format.extent210-220-
dc.language.isoeng-
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.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionTechnological Research Center-
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)-
dc.contributor.institutionFaculty of Engineering-
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.identifier.doi10.1007/978-3-642-12712-0_19-
dc.identifier.wosWOS:000279020400019-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.identifier.scopus2-s2.0-77952364349-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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