Abstract
In this work we present an evolutionary framework for colorimetric characterization of scanners. The problem consists in finding a mapping from the RGB space (where points indicate how a color stimulus is produced by a given device) to their corresponding values in the CIELAB space (where points indicate how the color is perceived in standard, i.e. device independent, viewing conditions). The proposed framework is composed by two phases: in the first one we use genetic programming for assessing a characterizing polynomial; in the second one we use genetic algorithms to assess suitable coefficients of that polynomial. Experimental results are reported to confirm the effectiveness of our framework with respect to a set of methods in the state of the art. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Bianco, S., Gasparini, F., Schettini, R., & Vanneschi, L. (2008). An evolutionary framework for colorimetric characterization of scanners. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 245–254). https://doi.org/10.1007/978-3-540-78761-7_25
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