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Colors of the Sublunar

Generic red, green, and blue images can be regarded as data sources of coarse (three bins) local spectra, typical data volumes are 10(4) to 10(7) spectra. Image data bases often yield hundreds or thousands of images, yielding data sources of 10(9) to 10(10) spectra. There is usually no calibration,...

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Detalles Bibliográficos
Autores principales: Koenderink, Jan, van Doorn, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624368/
https://www.ncbi.nlm.nih.gov/pubmed/28989697
http://dx.doi.org/10.1177/2041669517733484
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author Koenderink, Jan
van Doorn, Andrea
author_facet Koenderink, Jan
van Doorn, Andrea
author_sort Koenderink, Jan
collection PubMed
description Generic red, green, and blue images can be regarded as data sources of coarse (three bins) local spectra, typical data volumes are 10(4) to 10(7) spectra. Image data bases often yield hundreds or thousands of images, yielding data sources of 10(9) to 10(10) spectra. There is usually no calibration, and there often are various nonlinear image transformations involved. However, we argue that sheer numbers make up for such ambiguity. We propose a model of spectral data mining that applies to the sublunar realm, spectra due to the scattering of daylight by objects from the generic terrestrial environment. The model involves colorimetry and ecological physics. Whereas the colorimetry is readily dealt with, one needs to handle the ecological physics with heuristic methods. The results suggest evolutionary causes of the human visual system. We also suggest effective methods to generate red, green, and blue color gamuts for various terrains.
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spelling pubmed-56243682017-10-06 Colors of the Sublunar Koenderink, Jan van Doorn, Andrea Iperception Article Generic red, green, and blue images can be regarded as data sources of coarse (three bins) local spectra, typical data volumes are 10(4) to 10(7) spectra. Image data bases often yield hundreds or thousands of images, yielding data sources of 10(9) to 10(10) spectra. There is usually no calibration, and there often are various nonlinear image transformations involved. However, we argue that sheer numbers make up for such ambiguity. We propose a model of spectral data mining that applies to the sublunar realm, spectra due to the scattering of daylight by objects from the generic terrestrial environment. The model involves colorimetry and ecological physics. Whereas the colorimetry is readily dealt with, one needs to handle the ecological physics with heuristic methods. The results suggest evolutionary causes of the human visual system. We also suggest effective methods to generate red, green, and blue color gamuts for various terrains. SAGE Publications 2017-09-29 /pmc/articles/PMC5624368/ /pubmed/28989697 http://dx.doi.org/10.1177/2041669517733484 Text en © The Author(s) 2017 http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Article
Koenderink, Jan
van Doorn, Andrea
Colors of the Sublunar
title Colors of the Sublunar
title_full Colors of the Sublunar
title_fullStr Colors of the Sublunar
title_full_unstemmed Colors of the Sublunar
title_short Colors of the Sublunar
title_sort colors of the sublunar
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624368/
https://www.ncbi.nlm.nih.gov/pubmed/28989697
http://dx.doi.org/10.1177/2041669517733484
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