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dc.contributor.authorAntela, K.
dc.contributor.authorMorales-Rubio, A.
dc.contributor.authorBesada, Cristina
dc.contributor.authorTarancón, Paula
dc.contributor.authorCervera, M. L.
dc.contributor.authorLuque, M. J.
dc.contributor.editorGene, A.
dc.date.accessioned2021-06-15T10:00:53Z
dc.date.available2021-06-15T10:00:53Z
dc.date.issued2020es
dc.identifier.citationAntela, 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.es
dc.identifier.urihttp://hdl.handle.net/20.500.11939/7423
dc.description.abstractAim: 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 [1] compromising performance in color-discrimination-based everyday tasks [2], which may be compensated by procedures designed to optimize image color gamuts to minimize color confusion [3]. Given that red-green defectives retain normal discrimination along the blue-yellow axis in color space [1], 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 [4]. The modified palette was obtained by exchanging the values of the red-green (a*) and blue-yellow (b*) descriptors (Fig. 1).es
dc.language.isoenes
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectColor transformes
dc.titleColor transform to optimize fruit ripeness discrimination in dichromatses
dc.typeconferenceObjectes
dc.authorAddressbesada_cri@gva.eses
dc.entidadIVIACentro de Tecnología Post-recolecciónes
dc.page.final84es
dc.page.initial83es
dc.relation.conferenceDate2020-11-23
dc.relation.conferenceNameV Congreso Internacional de Jóvenes Optometristases
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrovocRipenesses


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