On the Robustness of Fuzzy-Genetic Colour Contrast Fusion with Variable Colour Depth
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Colour object classification using the fusion of visible and near-infrared spectra
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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This paper presents an application of a Fuzzy Colour Contrast Fusion (FFCF) algorithm in compensating for reduced colour depth representation of a colour image while maintaining efficient colour sensitivity that suffices for accurate real-time colour-based object recognition. We investigate the effects of applying fuzzy colour contrast rules to varying colour depth as we extract the optimal rule combination. The experiments were performed using the robot soccer game set-up with spatially varying illumination intensities on the scene. Interestingly, our results show that for most cases, colour depth reduction could actually improve colour classification via a pie-slice technique, in a modified rg-chromaticity colour space. For 6 different colours, the algorithm was able to yield 6.5% higher overall accuracy with only one-twelfth of LUT size than the full colour depth LUT.