Abstract
This paper contributes in colour classification under dynamically changing illumination, extending further the capabilities of our previous works on Fuzzy Colour Contrast Fusion (FCCF), FCCF-Heuristic Assisted Genetic Algorithm (HAGA) for automatic colour classifier calibration and Variable Colour Depth (VCD). All the aforementioned algorithms were proven to accurately in real-time with a pie-slice technique. However, the pie-slice classifier is the accuracy-limiting factor in these systems. Although it is possible to address this problem by using a more complex shape for specifying the colour decision region, this would only increase the chances of overfitting. We propose a hybrid colour classification system that automatically searches for the best colour space for classifying any target colour. Moreover, this paper also investigates the general selection of training sets to get a better understanding of the generalisation capability of FCCF-HAGA. The experiments used a professional Munsell ColorChecker Chart with extreme illumination conditions where the colour channels start hitting their dynamic range limits.
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Shin, H., Reyes, N.H., Barczak, A.L. (2010). A Hybrid Fuzzy-Genetic Colour Classification System with Best Colour Space Selection under Dynamically-Changing Illumination. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_36
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DOI: https://doi.org/10.1007/978-3-642-17534-3_36
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