Abstract
We present a robust fuzzy colour processing system with automatic rule extraction and colour descriptors calibration for accurate colour object recognition and tracking in real-time. The system is anchored on the fusion of fuzzy colour contrast rules that operate on the red, green and blue channels independently and adaptively to compensate for the effects of glare, shadow, and illumination variations in an indoor environment. The system also utilises a pie-slice colour classification technique in a modified rg-chromaticity space. Now, colour operations can be defined linguistically to allow a vision system to discriminate between similarly coloured objects more effectively. The validity and generality of the proposed fuzzy colour processing system is analysed by examining the complete mapping of the fuzzy colour contrast rules for each target colour object under different illumination intensities with the presence of similarly coloured objects. The colour calibration algorithm is able to extract colour descriptors in a matter of seconds as compared to manual calibration usually taking hours to complete. Using the robot soccer environment as a test bed, the algorithm is able to calibrate colours with excellent accuracy.
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Playne, D.P., Mehta, V.D., Reyes, N.H., Barczak, A.L.C. (2008). Hybrid Fuzzy Colour Processing and Learning. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_40
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DOI: https://doi.org/10.1007/978-3-540-69162-4_40
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