Training Data Selection Study for Surface Colour Measurement Data Correlation
Modeling random and systematic spectrophotometric errors and the use of multiple regression analysis improved successfully the agreement between instruments since the early 1980s. This was in particular the case if they were regressed on each wavelength. Recently a new model based on band pass error was developed. It was said to outperform other models inherent of more errors in the equation. This paper shows that this was hardly the case.
Furthermore, it was evident that all models performed best when training and testing samples were the same. This showed clearly the dependency of the model on the physical properties of the samples used for training. Using other materials for testing resulted in just little improvement.
It was then of interest to determine which and how many samples were needed for training the model while maintaining a good performance. A method was found to reduce the number of training samples from a larger population of the Munsell Color Book. This resulted in a training set of 20 samples compared to 245 colour samples for modeling the correlation between two instruments with similar results.
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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