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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
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]
Total Authors: 5
Affiliation:
[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
Total Affiliations: 4
Document type: Journal article
Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 40, n. 2, p. 590-597, FEB 1 2013.
Web of Science Citations: 28
Abstract

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)

FAPESP's process: 11/14058-5 - Exploring Sequential Learning Approaches for Optimum-Path Forest
Grantee:Rodrigo Yuji Mizobe Nakamura
Support Opportunities: Scholarships in Brazil - Master