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Comparison of Intelligent Classification Techniques Applied to Marble Classification

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Image Analysis and Recognition (ICIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3212))

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Abstract

Automatic marbles classification based on their visual appearance is an important industrial issue. However, there is no definitive solution to the problem, mainly due to the presence of randomly distributed high number of different colors and due to the subjective evaluation made by human experts. In this paper, we present a study of soft computing classification algorithms, which proved to be a valuable tool to be applied in this type of problems. Fuzzy, neural, simulated annealing, genetic and combinations of these approaches are compared. Color and vein classification of marbles are compared. The combination of fuzzy classifiers optimized by genetic algorithms revealed to be the best classifier for this application.

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Author information

Authors and Affiliations

  1. Instituto Superior Técnico Dept. of Mechanical Engineering, GCAR/IDMEC, Technical University of Lisbon, 1049-001, Lisbon, Portugal

    João M. C. Sousa & João R. Caldas Pinto

Authors
  1. João M. C. Sousa
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  2. João R. Caldas Pinto
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Editor information

Editors and Affiliations

  1. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

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© 2004 Springer-Verlag Berlin Heidelberg

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Sousa, J.M.C., Pinto, J.R.C. (2004). Comparison of Intelligent Classification Techniques Applied to Marble Classification. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_97

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  • DOI: https://doi.org/10.1007/978-3-540-30126-4_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23240-7

  • Online ISBN: 978-3-540-30126-4

  • eBook Packages: Springer Book Archive

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