Influence of the Size of the Training Set on Colour Measurements Performed using a Multispectral Imaging System
The main aim of this work is analyzing the influence of the size of the training set on colour measurements performed using an imaging system based on a CCD camera. In order to perform this study, training sets of different size were obtained using an easy selection method, based on differences in a* and b* CIELAB coordinates between the selected colour samples from an initial training set.
Two configuration of the imaging system have been used: a colorimetric configuration with 3 acquisition channels, and a multispectral configuration with 7 acquisition channels. Very similar results were obtained for both configurations, and it has been proved that the improvement in accuracy of colour measurements by increasing the size of the training set is limited, probably due to the fact that the expected improvement due to the increase of information is made up by the increase of errors accumulated in adjustment. Furthermore, a proper selection, uniform over the colour gamut available, of a low number of samples (40) allows to characterize the imaging system as well as a greater set of samples, for instance, the whole useful set of colour patches (166) of the GretagMacbeth ColorChecker DC chart.
Document Type: Research Article
Publication date: January 1, 2008
Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.
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