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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/71689
Title: 
Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest
Author(s): 
Institution: 
  • Universidade Estadual Paulista (UNESP)
  • Technological Research Center
  • Universidade Estadual de Campinas (UNICAMP)
  • Faculty of Engineering
ISSN: 
  • 0302-9743
  • 1611-3349
Abstract: 
In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.
Issue Date: 
21-May-2010
Citation: 
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6026 LNCS, p. 210-220.
Time Duration: 
210-220
Keywords: 
  • Cast irons
  • Image segmentation
  • Materials science
  • Microstructural evaluation
  • Supervised classification
  • Ferrous alloys
  • Forest classifiers
  • Kernel mapping
  • Malleable cast iron
  • Micro-structural
  • Microscopic image
  • Radial basis functions
  • Segmented images
  • Damping
  • Digital image storage
  • Iron
  • Malleable iron castings
  • Radial basis function networks
  • Support vector machines
  • Cast iron
Source: 
http://dx.doi.org/10.1007/978-3-642-12712-0_19
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/71689
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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