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Empirical evaluation of computational models of lightness perception
Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes simil...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772371/ https://www.ncbi.nlm.nih.gov/pubmed/36543784 http://dx.doi.org/10.1038/s41598-022-22395-7 |
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author | Nedimović, Predrag Zdravković, Sunčica Domijan, Dražen |
author_facet | Nedimović, Predrag Zdravković, Sunčica Domijan, Dražen |
author_sort | Nedimović, Predrag |
collection | PubMed |
description | Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MIR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White’s illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system. |
format | Online Article Text |
id | pubmed-9772371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97723712022-12-23 Empirical evaluation of computational models of lightness perception Nedimović, Predrag Zdravković, Sunčica Domijan, Dražen Sci Rep Article Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MIR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White’s illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system. Nature Publishing Group UK 2022-12-21 /pmc/articles/PMC9772371/ /pubmed/36543784 http://dx.doi.org/10.1038/s41598-022-22395-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nedimović, Predrag Zdravković, Sunčica Domijan, Dražen Empirical evaluation of computational models of lightness perception |
title | Empirical evaluation of computational models of lightness perception |
title_full | Empirical evaluation of computational models of lightness perception |
title_fullStr | Empirical evaluation of computational models of lightness perception |
title_full_unstemmed | Empirical evaluation of computational models of lightness perception |
title_short | Empirical evaluation of computational models of lightness perception |
title_sort | empirical evaluation of computational models of lightness perception |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772371/ https://www.ncbi.nlm.nih.gov/pubmed/36543784 http://dx.doi.org/10.1038/s41598-022-22395-7 |
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