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

Autor(es)

Papa, Joao P. ^[1] ; Nakamura, Rodrigo Y. M. ^[1] ; de Albuquerque, Victor Hugo C. ^[2] ; Falcao, Alexandre X. ^[3] ; Tavares, Joao Manuel R. S. ^[4]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, Dept Comp, Bauru - Brazil

^[2] Univ Fortaleza, Programa Posgrad Informat Aplicada, Fortaleza, Ceara - Brazil

^[3] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil

^[4] Univ Porto, Fac Engn, P-4100 Oporto - Portugal

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 40, n. 2, p. 590-597, FEB 1 2013.
• Citações Web of Science: 28

Resumo

The 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. (AU)

• Processo FAPESP: 11/14058-5 - Explorando Abordagens de Aprendizado Sequencial para Floresta de Caminhos Ótimos
• Beneficiário: Rodrigo Yuji Mizobe Nakamura
• Modalidade de apoio: Bolsas no Brasil - Mestrado