Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

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dc.contributor.authorAlbuquerque, Victor H. C.
dc.contributor.authorNakamura, Rodrigo Y. M. [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorSilva, Cleiton C.
dc.contributor.authorTavares, João Manuel R. S.
dc.contributor.institutionUniversidade de Fortaleza (UNIFOR)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal do Ceará (UFC)
dc.date.accessioned2014-05-27T11:26:23Z
dc.date.available2014-05-27T11:26:23Z
dc.date.issued2012-02-13
dc.description.abstractDuplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.en
dc.description.affiliationUniversidade de Fortal̀eza Centro de Ciências Tecnológicas, Fortaleza
dc.description.affiliationDepartamento de Computação UNESP-Universidade Estadual Paulista, Bauru
dc.description.affiliationDepartamento de Engenharia Metalúrgica e Materiais Universidade Federal do Ceará, Fortaleza
dc.description.affiliationUniversidade do Porto Faculdade de Engenharia, Porto
dc.description.affiliationUnespDepartamento de Computação UNESP-Universidade Estadual Paulista, Bauru
dc.format.extent161-166
dc.identifierhttp://www.crcpress.com/product/isbn/9780415683951
dc.identifier.citationComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166.
dc.identifier.lattes9039182932747194
dc.identifier.scopus2-s2.0-84856731518
dc.identifier.urihttp://hdl.handle.net/11449/73190
dc.language.isoeng
dc.relation.ispartofComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAutomatic segmentations
dc.subjectBayesian classifier
dc.subjectChemical compositions
dc.subjectMachine learning techniques
dc.subjectPattern recognition techniques
dc.subjectRecognition rates
dc.subjectSteel quality
dc.subjectSuperduplex stainless steels
dc.subjectImage processing
dc.subjectMechanical properties
dc.subjectMedical image processing
dc.subjectPattern recognition
dc.subjectStainless steel
dc.titleAutomatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metalen
dc.typeTrabalho apresentado em evento
unesp.author.lattes9039182932747194
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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