This thesis presents a complete self-calibrating illumination intensity-invariant colour classification system. We extend a novel fuzzy colour processing tech- nique called Fuzzy Colour Contrast Fusion (FCCF) by combining it with a Heuristic- assisted Genetic Algorithm (HAGA) for automatic fine-tuning of colour descriptors. Furthermore, we have improved FCCF’s efficiency by processing colour channels at varying colour depths in search for the optimal ones. In line with this, we intro- duce a reduced colour depth representation of a colour image while maintaining efficient colour sensitivity that suffices for accurate real-time colour-based object recognition. We call the algorithm Variable Colour Depth (VCD) and we propose a technique for building and searching a VCD look-up table (LUT). The first part of this work investigates the effects of applying fuzzy colour contrast rules to vary- ing colour depths as we extract the optimal rule combination for any given target colour exposed under changing illumination intensities. The second part introduces the HAGA-based parameter-optimisation for automatically constructing accurate colour classifiers. Our results show that for all cases, the VCD algorithm, combined with HAGA for parameter optimisation improve colour classification via a pie-slice colour classifier.For 6 different target colours, the hybrid algorithm was able to yield 17.63% higher overall accuracy as compared to the pure fuzzy approach. Fur- thermore, it was able to reduce LUT storage space by 78.06% as compared to the full-colour depth LUT.
| Identifer | oai:union.ndltd.org:ADTP/269425 |
| Date | January 2009 |
| Creators | Shin, Heesang |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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