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Natural Color Recognition Using Fuzzification and a Neural Network for Industrial Applications

  • Yountae Kim
  • Hyeon Bae
  • Sungshin Kim
  • Kwang-Baek Kim
  • Hoon Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

The Conventional methods of color separation in computer-based machine vision offer only weak performance because of environmental factors such as light source, camera sensitivity, and others. In this paper, we propose an improved color separation method using fuzzy membership for feature implementation and a neural network for feature classification. In addition, we choose HLS color coordination. The HLS includes hue, light, and saturation. There are the most human-like color recognition elements. A proposed color recognition algorithm is applied to a line order detection system of harness. The detection system was designed and implemented as a testbed to evaluate the physical performance. The proposed color separation algorithm is tested with different kinds of harness line.

Keywords

Fuzzy Membership Fuzzy Measure Color Area Fuzzy Entropy Color Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Wang, Z., Klir, G.T.: Fuzzy Measure Theory. Plenum Press, New York (1992)MATHGoogle Scholar
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    Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yountae Kim
    • 1
  • Hyeon Bae
    • 1
  • Sungshin Kim
    • 1
  • Kwang-Baek Kim
    • 2
  • Hoon Kang
    • 3
  1. 1.School of Electrical and Computer EngineeringPusan National UniversityBusanKorea
  2. 2.Department of Computer EngineeringSilla University 
  3. 3.School of Electrical and Electronics EngineeringChung-Ang University 

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