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Natural color recognition using fuzzification and a neural network for industrial applications
- Authors
- Kim, Y.; Bae, H.; Kim, S.; Kim, K.-B.; Kang, H.
- Issue Date
- Jun-2006
- Publisher
- Springer Verlag
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.3973 LNCS, pp 991 - 996
- Pages
- 6
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 3973 LNCS
- Start Page
- 991
- End Page
- 996
- ISSN
- 0302-9743
1611-3349
- 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. © Springer-Verlag Berlin Heidelberg 2006.
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