Contours produced by internal specular interreflections provide visual information for the perception of glass materials

Todd, James T.; Norman, J. Farley

doi:10.1167/jov.20.10.12

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…There are numerous studies quantifying and perceptually evaluating the visual qualities of metallic surfaces and coatings, considering qualities such as colour, gloss 7 , scatter, brilliance, and lustre 8 , as well as ‘visual texture’ such as glitter, glint, and sparkle 9 , 10 . Recently, attention has focused more on how the physical properties of objects and their lighting environment might give rise to a metallic appearance 11 – 13 . In the present study, we simulated metal objects through physically-based rendering, which varied according to two physical properties: metal smoothness, and the bumpiness of a transparent coating.…”

Section: Introductionmentioning

confidence: 99%

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

Harvey

Smithson

2021

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Harvey

Smithson

2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…There are numerous studies quantifying and perceptually evaluating the visual qualities of metallic surfaces and coatings, considering qualities such as colour, gloss 7 , scatter, brilliance, and lustre 8 , as well as 'visual texture' such as glitter, glint, and sparkle 9,10 . Recently, attention has focused more on how the physical properties of objects and their lighting environment might give rise to a metallic appearance [11][12][13] . In the present study, we simulated metal objects through physically-based rendering, which varied according to two physical properties: metal smoothness, and the bumpiness of a transparent coating.…”

Section: Introductionmentioning

confidence: 99%

Modeling Metallicity: Low Level Visual Features Support Robust Material Perception

Harvey

Smithson

2021

Preprint

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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 manipulation 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 judging metal smoothness, coating bumpiness, and 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.

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A dissociation between the use of implicit and explicit priors in perceptual inference

Bévalot,

Meyniel

2023

Preprint

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The brain constantly uses prior knowledge of the statistics of its environment to shape perception. These statistics are often implicit (not directly observable) and gradually learned from observation; but they can also be explicitly communicated to the observer, especially in humans. In value-based decision-making, these priors are treated differently depending on their implicit or explicit origin creating the "experience-description gap". Here, we show that the same distinction also applies to perception. We created a pair of categorization tasks with implicit and explicit priors respectively, and manipulated the strength of priors and sensory likelihood within the same human subjects. Perceptual decisions were influenced by priors in both tasks, and subjects updated their priors in the implicit task as the true statistics changed. Using Bayesian models of learning and perception, we found that the weight of the sensory likelihood in perceptual decisions was highly correlated across subjects between tasks, and slightly stronger in the implicit task. By contrast, the weight of priors was much less correlated across tasks, and it increased markedly from the explicit task to the implicit task. The same conclusion holds when using the subjects' reported priors. Model comparison also showed that different computations underpinned perceptual decisions depending on the origin of the priors. Taken together, those results support a dissociation in perceptual inference between the use of implicit and explicit priors. This conclusion could resolve conflicting results generated by the indiscriminate use of implicit and explicit priors when studying perception in healthy subjects and patients.

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Contours produced by internal specular interreflections provide visual information for the perception of glass materials

Cited by 8 publications

References 36 publications

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

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

Modeling Metallicity: Low Level Visual Features Support Robust Material Perception

A dissociation between the use of implicit and explicit priors in perceptual inference

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