Natural color recognition using fuzzification and a neural network for industrial applications

  • Authors:

  • Yountae Kim;Hyeon Bae;Sungshin Kim;Kwang-Baek Kim;Hoon Kang

  • Affiliations:

  • School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;School of Electrical and Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Silla University;School of Electrical and Electronics Engineering, Chung-Ang University

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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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.