Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

dc.creatorPapa, JP
dc.creatorNakamura, RYM
dc.creatorde Albuquerque, VHC
dc.creatorFalcao, AX
dc.creatorTavares, JMRS
dc.date2013
dc.dateFEB 1
dc.date2014-07-30T14:00:30Z
dc.date2015-11-26T16:33:26Z
dc.date2014-07-30T14:00:30Z
dc.date2015-11-26T16:33:26Z
dc.date.accessioned2018-03-28T23:15:19Z
dc.date.available2018-03-28T23:15:19Z
dc.identifierExpert Systems With Applications. Pergamon-elsevier Science Ltd, v. 40, n. 2, n. 590, n. 597, 2013.
dc.identifier0957-4174
dc.identifierWOS:000310945000020
dc.identifier10.1016/j.eswa.2012.07.062
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/56431
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/56431
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1270856
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThe automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. (C) 2012 Elsevier Ltd. All rights reserved.
dc.description40
dc.description2
dc.description590
dc.description597
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP), in Brazil through a DCR Grant [35.0053/2011.1]
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCNPq [303673/2010-9, 303182/2011-3]
dc.descriptionFAPESP [2009/16206-1, 2011/14058-5]
dc.descriptionFundacao Cearense de Apoio ao Desenvolvimento Cientifico e Tecnologico (FUNCAP), in Brazil through a DCR Grant [35.0053/2011.1]
dc.languageen
dc.publisherPergamon-elsevier Science Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationExpert Systems With Applications
dc.relationExpert Syst. Appl.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectMaterial characterization
dc.subjectComputer methods
dc.subjectComputer classifiers
dc.subjectOtsu's method
dc.subjectNodular
dc.subjectGray and malleable cast irons
dc.subjectOptimum-path Forest
dc.subjectCast-iron
dc.subjectClassification
dc.subjectTransform
dc.titleComputer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials
dc.typeArtículos de revistas


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