Natural Color Recognition Using Fuzzification and a Neural Network for Industrial Applications
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.
KeywordsFuzzy Membership Fuzzy Measure Color Area Fuzzy Entropy Color Separation
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- 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)Google Scholar
- 2.Palus, H., Bereska, D.: The Comparison between Transformations from RGB Colour Space to IHS Colour Space, Used For Object Recognition. In: Fifth International Conference on Image Processing and Its Applications, pp. 825–827 (1995)Google Scholar