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

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorAlbuquerque, Victor H. C.
dc.creatorNakamura, Rodrigo Y. M.
dc.creatorPapa, João Paulo
dc.creatorSilva, Cleiton C.
dc.creatorTavares, João Manuel R. S.
dc.date2014-05-27T11:26:23Z
dc.date2016-10-25T18:36:36Z
dc.date2014-05-27T11:26:23Z
dc.date2016-10-25T18:36:36Z
dc.date2012-02-13
dc.date.accessioned2017-04-06T01:57:20Z
dc.date.available2017-04-06T01:57:20Z
dc.identifierComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, p. 161-166.
dc.identifierhttp://hdl.handle.net/11449/73190
dc.identifierhttp://acervodigital.unesp.br/handle/11449/73190
dc.identifier2-s2.0-84856731518
dc.identifierhttp://www.crcpress.com/product/isbn/9780415683951
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/894010
dc.descriptionDuplex 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.
dc.languageeng
dc.relationComputational Vision and Medical Image Processing, Proceedings of VipIMAGE 2011 - 3rd ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing
dc.rightsinfo:eu-repo/semantics/closedAccess
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 metal
dc.typeOtro


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