Investigation of colour constancy using blind signal separation and physics-based image modelling

iii The methodology exploited to evaluate the performance of the framework involves the development of algorithms, their implementation in software, and their assessment using welldesigned experiments anchored on quantitative performance measurement methods. The goodness-of-fit coefficient (GFC) is used to evaluate the performance of the framework, by measuring the degree of similarity between the estimated spectral distribution and a known reference. Values of GFC range between 0 and 1; a higher value representing a higher degree of similarity. Using an image data set generated by the author, compared to the manufacturer’s specifications, the estimated ISPD has an average GFC value equal to 0.9830 and 0.9215 for two light sources with colour temperature of 5500 K and 2900 K, respectively. The average GFC of the estimated ISPD improves significantly by 2.9% when the explicit specular image component is used instead of mixed image components. Furthermore, using Foster et al’s image data set (a set of hyperspectral images of natural scenes which was collected by Foster, Nascimento, and Amano), the ISPD is estimated using the mixed image components for other light sources with different colour temperatures. The results show that the estimated ISPD has an average value of the GFC equal to 0.9986 compared to the measured illumination. Using the data set collected by the author of this thesis, the surface spectral reflectance is estimated at individual pixels of an object illuminated by two alternative light sources with colour temperatures of 5500 K and 2900 K. A comparative assessment shows that the spectral reflectance, estimated for each given surface, has almost the same spectral signature for the two light sources. The comparison between the surface spectral reflectance estimates corresponding to the two light sources gives an average GFC value which ranges from 0.9611 to 0.9887, depending on the type of the blind separation technique that is used (i.e. the spatially constrained FastICA technique and the technique developed by Umeyama and Godin). Given that the surface spectral reflectance is the output of the last stage of the framework, which depends on the output of the previous two stages, therefore the GFC measured for surface spectral reflectance reflects the performance of the whole framework. The high GFC values mean that the estimates of surface reflectance under the two light sources are very similar, despite the differences between the two illuminants. This similarity implies that the extracted surface reflectance is significantly independent of illumination characteristics, hence showing that the proposed framework achieved a significant degree of colour constancy. Moreover, the observed results show a statistically significant improvement in the accuracy of the estimated surface spectral reflectance by 2.6% in terms of average GFC value when the explicitly extracted diffuse image component is used instead of the mixed image components. Compared to the surface spectral reflectance measurements included in Foster et al’s image data set, the surface spectral reflectance estimated using the mixed image components has an average GFC value equal to 0.9608.