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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 991–996Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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References

  1. Chien, B., Cheng, M.: A Color Image Segmentation Approach Based on Fuzzy Similarity Measure. Proceedings of IEEE International Conf. on Fuzzy Systems 1, 449–454 (2002)

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  3. Wang, Z., Klir, G.T.: Fuzzy Measure Theory. Plenum Press, New York (1992)

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Pusan National University, Changjeon-dong, Keumjeong-ku, Busan, 609-735, Korea

    Yountae Kim, Hyeon Bae & Sungshin Kim

  2. Department of Computer Engineering, Silla University,  

    Kwang-Baek Kim

  3. School of Electrical and Electronics Engineering, Chung-Ang University,  

    Hoon Kang

Authors
  1. Yountae Kim
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  2. Hyeon Bae
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  3. Sungshin Kim
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  4. Kwang-Baek Kim
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  5. Hoon Kang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Kim, Y., Bae, H., Kim, S., Kim, KB., Kang, H. (2006). Natural Color Recognition Using Fuzzification and a Neural Network for Industrial Applications. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_145

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  • DOI: https://doi.org/10.1007/11760191_145

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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