Color transform to optimize fruit ripeness discrimination in dichromats
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AutorAntela, K.; Morales-Rubio, A.; Besada, Cristina; Tarancón, Paula; Cervera, M. L.; Luque, M. J.
Cita bibliográficaAntela, K., Morales-Rubio, A., Besada, C., Tarancón, P., Cervera, M.L., & Luque, M.J. (2020). Color transform to optimize fruit ripeness discrimination in dichromats. En: Gene, A., Luque, MJ., Sañudo, F., Bueno, I., Herández, R., García, MC., Esteve, J. & Díez, MA. (Eds). V Congreso Internacional de Jóvenes Optometristas. pp: 83-84.
Aim: To develop and test a color transform for red-green color defectives to enhance tomatoipeness judgements. Experimental Method: Congenital protan and deutan color defectives suffer sensitivity losses in the red-green mechanism  compromising performance in color-discrimination-based everyday tasks , which may be compensated by procedures designed to optimize image color gamuts to minimize color confusion . Given that red-green defectives retain normal discrimination along the blue-yellow axis in color space , we propose a simple procedure to recode redgreen color differences in CIELAB color space as blue-yellow color differences, to allow red-green defectives to correctly judge the ripeness of tomatoes. An agricultural cooperative of Perelló supplied and classified by color the tomato samples in a controlled manner in four standard ripeness stages. Sample color was measured with a portable Minolta CR-300 colorimeter (Minolta Co. Ltd, Osaka, Japan). Tomatoes were photographed with a Smartphone (Samsung Galaxy S7 edge model SMG935F with a 12.2 MP camera). RGB values of the image were transformed to XYZ values using Matlab’s sRGB transform, and then to CIEL*a*b*, using as reference white a white sample illuminated as the samples. Dichromatic perception of the images was simulated by the corresponding pair algorithm . The modified palette was obtained by exchanging the values of the red-green (a*) and blue-yellow (b*) descriptors (Fig. 1).