Practical colour constancy
The ability of a vision system to diminish, or in the ideal case, remove, the effect of the illumination, and therefore “see” the physical scene more precisely, is called colour constancy. Interest in human vision, as well as robotics and image reproduction applications, has led to much research into computational methods to achieve colour constancy. In order to serve the needs of the proposed applications, it is necessary to develop and test computational colour constancy algorithms for real image data. This practical development of computational colour constancy is the focus of this work. 
In order to study and to use computational models for real image data, it is necessary to develop a model of the physical characteristics of the vision system of interest. 
The next part of this study is the comprehensive testing of current colour constancy algorithms. The results from this study, as well as the testing paradigm that was developed, provide a foundation for the rest of the work, which specifically sets out to improve computational colour constancy on image data. 
The first area studied to improve computational colour constancy is the sensor sharpening method of Finlayson et al. In this work, I set out to test the degree to which sensor sharpening can help current colour constancy algorithms. I find that the current sharpening methods do not address the needs of this domain, and thus as part of this work, I propose a pragmatic new sharpening method. 
I then propose several improvements to variants of Forsyth's CRULE algorithm. The first is a new method of choosing a solution from the feasible set. The second reduces the reliance of the method on the diagonal model. This method enables CRULE to be extended to work with fluorescent surfaces, and is the first algorithm to deal with such input. 
A third modification to gamut mapping algorithms proposed in this work allows them to use specularities to an advantage. Specularities have long been put to use by colour constancy algorithms, but existing algorithms that use specular information are limited in that they require such information to be present. The method presented here combines the use of specular and non-specular information. 
Another algorithm chosen for close study is Finlayson et al.'s chromaticity based Colour by Correlation method. This method is attractive because, unlike the CRULE derivatives, it can take advantage of statistical information about the world. However, the comparison work done as part of this thesis indicates that the pixel brightness is also a very important source of information. (Abstract shortened by UMI.)