Color transform to optimize fruit ripeness discrimination in dichromats
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 [1] compromising performance in color-discrimination-based everyd...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Objeto de conferencia |
| Language: | Inglés |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11939/7423 |
| _version_ | 1855492284789817344 |
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| author | Antela, K. Morales-Rubio, A. Besada, Cristina Tarancón, Paula Cervera, M. L. Luque, M. J. |
| author2 | Gene, A. |
| author_browse | Antela, K. Besada, Cristina Cervera, M. L. Gene, A. Luque, M. J. Morales-Rubio, A. Tarancón, Paula |
| author_facet | Gene, A. Antela, K. Morales-Rubio, A. Besada, Cristina Tarancón, Paula Cervera, M. L. Luque, M. J. |
| author_sort | Antela, K. |
| collection | ReDivia |
| description | 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 [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). |
| format | Objeto de conferencia |
| id | ReDivia7423 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| record_format | dspace |
| spelling | ReDivia74232025-04-25T14:52:46Z Color transform to optimize fruit ripeness discrimination in dichromats Antela, K. Morales-Rubio, A. Besada, Cristina Tarancón, Paula Cervera, M. L. Luque, M. J. Gene, A. Color transform Ripeness 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 [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). 2021-06-15T10:00:53Z 2021-06-15T10:00:53Z 2020 conferenceObject Antela, 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. http://hdl.handle.net/20.500.11939/7423 en 2020-11-23 V Congreso Internacional de Jóvenes Optometristas Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess electronico |
| spellingShingle | Color transform Ripeness Antela, K. Morales-Rubio, A. Besada, Cristina Tarancón, Paula Cervera, M. L. Luque, M. J. Color transform to optimize fruit ripeness discrimination in dichromats |
| title | Color transform to optimize fruit ripeness discrimination in dichromats |
| title_full | Color transform to optimize fruit ripeness discrimination in dichromats |
| title_fullStr | Color transform to optimize fruit ripeness discrimination in dichromats |
| title_full_unstemmed | Color transform to optimize fruit ripeness discrimination in dichromats |
| title_short | Color transform to optimize fruit ripeness discrimination in dichromats |
| title_sort | color transform to optimize fruit ripeness discrimination in dichromats |
| topic | Color transform Ripeness |
| url | http://hdl.handle.net/20.500.11939/7423 |
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