In the modern digital society colors are often used to encode information. Nevertheless the selection of set of colors that maximizes class discriminability for nominal coding is a non-trivial problem. In this work we compare four different heuristics for the selection of sets of colors with fixed cardinality and maximum dissimilarity. The performance of each algorithm is evaluated both on single and multiple illuminants, on a sample of 1268 colors from the Munsell atlas, using ΔE 76 euclidean metrics on the perceptually uniform CIE L* a* b* space. Results are presented for color sets with cardinality up to 25. © 2013 Springer-Verlag.

Bianco, S., Citrolo, A. (2013). High contrast color sets under multiple illuminants. In Computational Color Imaging. CCIW 2013 (pp.133-142). Springer [10.1007/978-3-642-36700-7_11].

High contrast color sets under multiple illuminants

BIANCO, SIMONE
;
2013

Abstract

In the modern digital society colors are often used to encode information. Nevertheless the selection of set of colors that maximizes class discriminability for nominal coding is a non-trivial problem. In this work we compare four different heuristics for the selection of sets of colors with fixed cardinality and maximum dissimilarity. The performance of each algorithm is evaluated both on single and multiple illuminants, on a sample of 1268 colors from the Munsell atlas, using ΔE 76 euclidean metrics on the perceptually uniform CIE L* a* b* space. Results are presented for color sets with cardinality up to 25. © 2013 Springer-Verlag.
slide + paper
genetic algorithm; greedy algorithm; High contrast color set; local search; simulated annealing; Computer Science (all); Theoretical Computer Science
English
4th Computational Color Imaging Workshop, CCIW 2013
2013
Tominaga S., Schettini R., Trémeau A.
Computational Color Imaging. CCIW 2013
978-3-642-36699-4
2013
7786 LNCS
133
142
none
Bianco, S., Citrolo, A. (2013). High contrast color sets under multiple illuminants. In Computational Color Imaging. CCIW 2013 (pp.133-142). Springer [10.1007/978-3-642-36700-7_11].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/56715
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