Improving the performance of computer color matching procedures

Abstract

A premise was set up entailing the possibility of a synergistical combination of advantages of spectrophotometric and colorimetric matching procedures. Attempts were therefore made to test the performances of fifteen matching procedures, all based on the Kubelka-Munk theory, including two procedures utilizing the fundamental color stimulus R(FCS) of the spectral decomposition theory. Color differences CIE DeltaE(00) as well as concentration differences DeltaC(AVE) were used to theoretically rank the fifteen color matching procedures. Results showed that procedures based on R(FCS) were superior in accurately predicting colors and concentrations. Additionally, the metameric black component R(MB) of the decomposition theory also showed promise in predicting degrees of metamerism. This preliminary study, therefore, provides evidence for the premise of this investigation.

Hybrid biogeography based optimization for constrained optimal spot color matching

“…There are two major approaches for SCM, namely, the spectrophotometric approach 27 and the tristimulus approach. 8 Often attributed to Allen, 28 the tristimulus approach is based on the minimization of the CIE tristimulus differences between a target and a predicted match; however, two originally consistent colors may become inconsistent in other illuminant condition due to the metamerism phenomenon. In contrast to the tristimulus matching, the spectrophotometric approach seeks to match the wavelength spectrum of the target directly rather than the tristimulus values of the target, thus indirectly leads to small color differences, consequently, the matching result is no longer dependant on the illuminant condition as introduced in detail in Ref.…”

“…As mentioned in Ref. 8, the objective function of this constrained optimization problem may be formulated as follows:…”

“…However, it is recently found that SCM is in fact a nonlinear optimization problem . Karbasi et al stated that this problem is hard to solve due to its non‐convex, non‐smooth, and non‐differentiable nature. Therefore, fast and robust approaches are needed to obtain the optimal formula for SCM in real application.…”

Análisis de Algoritmos Empleados en los Procesos de Igualación del Color para Películas Poliméricas

“…Dicho cociente es calculado sustituyendo las reflectancias de las pinturas en cuestión, medidas por un espectrofotómetro, en la relación que resulta de despejar K/S de la ecuación (3). (Karbasi, et-al 2008) Una vez caracterizadas las pinturas se procede a determinar las concentraciones y combinaciones necesarias de las pinturas de la base de datos, tal que la mezcla reproduzca el valor K/S de la muestra según la ecuación (4). (Artigas, et al, 2002;Billmeyer, et al, 1973a) Modificación al Algoritmo tradicional para igualar colores…”

“…También se ha encontrado que la reflectancia esta ligada a la naturaleza del pigmento utilizado, es decir varía según el tipo, tamaño y forma del pigmento. (Curiel et-al, 2002;Gunde y Orel, 2000;Perera, 2005;Liu et-al, 2005) Ninguno de estos resultados ha sido considerado en el proceso de igualación del color, (Bondioli etal, 2006;Ishikawa-Nagai et-al, 2005;Karbasi et-al, 2008) motivo por el cual, en este trabajo se analiza la variación del color de una pintura creada a partir de la mezcla de pinturas pertenecientes a una base de datos, cuya formulación es parcialmente conocida, y una base de datos de quien solo se conoce la reflectancia, realizando el proceso considerando o no la interfase aire-pintura, con el fin de igualar el color de una pintura muestra cuya formulación es desconocida.…”