Low level visual features support robust material perception in the judgement of metallicity

Abstract The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to...

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Autores principales:	Joshua S. Harvey, Hannah E. Smithson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce486da491934597a12189c111a2f0a8
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spelling	oai:doaj.org-article:ce486da491934597a12189c111a2f0a82021-12-02T16:27:50ZLow level visual features support robust material perception in the judgement of metallicity10.1038/s41598-021-95416-62045-2322https://doaj.org/article/ce486da491934597a12189c111a2f0a82021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95416-6https://doaj.org/toc/2045-2322Abstract The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials—is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modeling that supports the use of image statistics heuristics in the judgement of metallicity—the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the exploration of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in perceiving differences in metal smoothness and coating bumpiness, and judging metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity.Joshua S. HarveyHannah E. SmithsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joshua S. Harvey Hannah E. Smithson Low level visual features support robust material perception in the judgement of metallicity
description	Abstract The human visual system is able to rapidly and accurately infer the material properties of objects and surfaces in the world. Yet an inverse optics approach—estimating the bi-directional reflectance distribution function of a surface, given its geometry and environment, and relating this to the optical properties of materials—is both intractable and computationally unaffordable. Rather, previous studies have found that the visual system may exploit low-level spatio-chromatic statistics as heuristics for material judgment. Here, we present results from psychophysics and modeling that supports the use of image statistics heuristics in the judgement of metallicity—the quality of appearance that suggests an object is made from metal. Using computer graphics, we generated stimuli that varied along two physical dimensions: the smoothness of a metal object, and the evenness of its transparent coating. This allowed for the exploration of low-level image statistics, whilst ensuring that each stimulus was a naturalistic, physically plausible image. A conjoint-measurement task decoupled the contributions of these dimensions to the perception of metallicity. Low-level image features, as represented in the activations of oriented linear filters at different spatial scales, were found to correlate with the dimensions of the stimulus space, and decision-making models using these activations replicated observer performance in perceiving differences in metal smoothness and coating bumpiness, and judging metallicity. Importantly, the performance of these models did not deteriorate when objects were rotated within their simulated scene, with corresponding changes in image properties. We therefore conclude that low-level image features may provide reliable cues for the robust perception of metallicity.
format	article
author	Joshua S. Harvey Hannah E. Smithson
author_facet	Joshua S. Harvey Hannah E. Smithson
author_sort	Joshua S. Harvey
title	Low level visual features support robust material perception in the judgement of metallicity
title_short	Low level visual features support robust material perception in the judgement of metallicity
title_full	Low level visual features support robust material perception in the judgement of metallicity
title_fullStr	Low level visual features support robust material perception in the judgement of metallicity
title_full_unstemmed	Low level visual features support robust material perception in the judgement of metallicity
title_sort	low level visual features support robust material perception in the judgement of metallicity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ce486da491934597a12189c111a2f0a8
work_keys_str_mv	AT joshuasharvey lowlevelvisualfeaturessupportrobustmaterialperceptioninthejudgementofmetallicity AT hannahesmithson lowlevelvisualfeaturessupportrobustmaterialperceptioninthejudgementofmetallicity
_version_	1718384018147246080

Low level visual features support robust material perception in the judgement of metallicity

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