&lt;title&gt;Psychophysically based model of surface gloss perception&lt;/title&gt;

Ferwerda, James A.; Pellacini, Fabio; Greenberg, Donald P.

doi:10.1117/12.429501

Cited by 114 publications

(121 citation statements)

References 20 publications

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“…An important question is how the activities of these neurons are related to the gloss perception. The relationship between the physical parameters of gloss and perceived gloss was examined in a previous psychophysical study, and a perceptually uniform gloss space was derived (Ferwerda et al, 2001). This perceptual gloss space is a two-dimensional space with a c (contrast gloss) axis and d (distinctness of gloss) axis.…”

Section: Relationship With Gloss Perceptionmentioning

confidence: 99%

Neural Selectivity and Representation of Gloss in the Monkey Inferior Temporal Cortex

Nishio

Goda²,

Komatsu³

2012

Journal of Neuroscience

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When we view an object, its appearance depends in large part on specific surface reflectance properties; among these is surface gloss, which provides important information about the material composition of the object and the fine structure of its surface. To study how gloss is represented in the visual cortical areas related to object recognition, we examined the responses of neurons in the inferior temporal (IT) cortex of the macaque monkey to a set of object images exhibiting various combinations of specular reflection, diffuse reflection, and roughness, which are important physical parameters of surface gloss. We found that there are neurons in the lower bank of the superior temporal sulcus that selectively respond to specific gloss. This neuronal selectivity was largely maintained when the shape or illumination of the object was modified and perceived glossiness was unchanged. By contrast, neural responses were significantly altered when the pixels of the images were randomly rearranged, and perceived glossiness was dramatically changed. The stimulus preference of these neurons differed from cell to cell, and, as a population, they systematically represented a variety of surface glosses. We conclude that, within the visual cortex, there are mechanisms operating to integrate local image features and extract information about surface gloss and that this information is systematically represented in the IT cortex, an area playing an important role in object recognition.

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Section: Relationship With Gloss Perceptionmentioning

confidence: 99%

Neural Selectivity and Representation of Gloss in the Monkey Inferior Temporal Cortex

Nishio

Goda²,

Komatsu³

2012

Journal of Neuroscience

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“…We observe a slight overestimation of gloss, although this deviation is in most cases smaller than the standard deviation. The standard deviation of perceived gloss in realistic images is equal to 0.036 on average, which corresponds to approximately one JND of contrast ∆c or two JND of sharpness ∆d (Ferwerda et al [2001] do not provide the value of a JND in the gloss dimension). Note however that the standard deviation is higher for the low contrast materials, for which subjects cannot clearly distinguish highlights.…”

Section: Resultsmentioning

confidence: 99%

“…However, our findings differ from these previous observations as we identify a correlation between the two dimensions for materials in the mid-gloss range (Section 6.2). Ferwerda et al [2001] measured the just-noticeable differences (JND) for the two dimensions of the gloss space as ∆c = 0.031 and ∆d = 0.017. …”

Section: Background On Gloss In Realistic Imagesmentioning

confidence: 99%

Gloss perception in painterly and cartoon rendering

et al. 2013

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MIT CSAILDepictions with traditional media such as painting and drawing represent scene content in a stylized manner. It is unclear however how well stylized images depict scene properties like shape, material and lighting. In this paper, we describe the first study of material perception in stylized images (specifically painting and cartoon) and use non photorealistic rendering algorithms to evaluate how such stylization alters the perception of gloss. Our study reveals a compression of the range of representable gloss in stylized images so that shiny materials appear more diffuse in painterly rendering, while diffuse materials appear shinier in cartoon images. From our measurements we estimate the function that maps realistic gloss parameters to their perception in a stylized rendering. This mapping allows users of NPR algorithms to predict the perception of gloss in their images. The inverse of this function exaggerates gloss properties to make the contrast between materials in a stylized image more faithful. We have conducted our experiment both in a lab and on a crowdsourcing website. While crowdsourcing allows us to quickly design our pilot study, a lab experiment provides more control on how subjects perform the task. We provide a detailed comparison of the results obtained with the two approaches and discuss their advantages and drawbacks for studies like ours. This is the authors version of the work. It is posted by permission of ACM for your personal use. Not for redistribution. The definite version will be published in ACM TOG.

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“…The knowledge about shininess is also essential to computer scientists who work on object rendering and visualization [4,5]. Realistic surface appearance is not only needed for computer aided design and manufacturing, but also for computer graphics in the entertainment industry.…”

Section: Introductionmentioning

confidence: 99%