Finding near optimum colour classifiers : genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth : a thesis presented to the Institute of Information and Mathematical Sciences in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand

Shin, Heesang

Finding near optimum colour classifiers : genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth : a thesis presented to the Institute of Information and Mathematical Sciences in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand

dc.contributor.author	Shin, Heesang
dc.date.accessioned	2009-10-28T01:38:03Z
dc.date.available	NO_RESTRICTION	en_US
dc.date.available	2009-10-28T01:38:03Z
dc.date.issued	2009
dc.description.abstract	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.	en_US
dc.identifier.uri	http://hdl.handle.net/10179/1096
dc.language.iso	en	en_US
dc.publisher	Massey University	en_US
dc.rights	The Author	en_US
dc.subject	Colour classifiers	en_US
dc.subject	Computer vision	en_US
dc.subject.other	Fields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processing::280208 Computer vision	en_US
dc.title	Finding near optimum colour classifiers : genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth : a thesis presented to the Institute of Information and Mathematical Sciences in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand	en_US
dc.type	Thesis	en_US
massey.contributor.author	Shin, Heesang
thesis.degree.discipline	Computer Science	en_US
thesis.degree.grantor	Massey University	en_US
thesis.degree.level	Masters	en_US
thesis.degree.name	Master of Science (M.Sc.)	en_US