Information about the spectral reflectance of a color surface is useful in many applications. Assuming that reflectance functions can be adequately approxi- mated by a linear combination of a small number of basis functions, we address here the recovery of a surface reflectance function, given the tristimulus values under one or more illuminants. Basis functions presenting different characteristics and cardinalities are investi- gated, and genetic algorithms are employed to optimize the estimation. Our analysis of a variety of standard datasets provides information about the ability of each set of basis functions we used to model generic reflec- tance spectra.

A Computational Strategy Exploiting Genetic Algorithms to Recover Color Surface Reflectance Functions

S Zuffi
2007

Abstract

Information about the spectral reflectance of a color surface is useful in many applications. Assuming that reflectance functions can be adequately approxi- mated by a linear combination of a small number of basis functions, we address here the recovery of a surface reflectance function, given the tristimulus values under one or more illuminants. Basis functions presenting different characteristics and cardinalities are investi- gated, and genetic algorithms are employed to optimize the estimation. Our analysis of a variety of standard datasets provides information about the ability of each set of basis functions we used to model generic reflec- tance spectra.
2007
Istituto per le Tecnologie della Costruzione - ITC
Genetic algorithms
Linear models
Reflectance function
Tristimulus values
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/27444
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