Visual Perception of Thick Transparent Materials

Fleming, Roland W.; Jäkel, Frank; Maloney, Laurence T.

doi:10.1177/0956797611408734

Cited by 94 publications

(118 citation statements)

References 23 publications

(26 reference statements)

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“…Most of these studies have focused on the visual estimation of the specific properties of materials (Anderson, 2011;Thompson, Fleming, Creem-Regehr, & Stefanucci, 2011;Zaidi, 2011), such as glossiness (Fleming, Dror, & Adelson, 2003;Motoyoshi & Matoba, 2012;Nishida & Shinya, 1998), translucency (Fleming & Bülthoff, 2005 Maloney, 2011;Motoyoshi, 2010), or roughness (Padilla, Drbohlav, Green, Spence, & Chantler, 2008;Pont & Koenderink, 2005;Pont & Koenderink, 2008). Taken together, these findings support the general idea that the human visual system can estimate the properties of materials from relatively low-level vision features.…”

Section: Introductionsupporting

confidence: 63%

Investigating perceptual qualities of static surface appearance using real materials and displayed images

Tanaka

Horiuchi

2015

Vision Research

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Recent experimental evidence supports the idea that human observers are good at recognizing and categorizing materials. Fleming et al. reported that perceptual qualities and material classes are closely related using projected images (Journal of Vision 13(8) (2013) 9). In this paper, we further investigated their findings using real materials and degraded image versions of the same materials. We constructed a real material dataset, as well as four image datasets by varying chromaticity (color vs. gray) and resolution (high vs. low) of the material images. To investigate the fundamental properties of materials' static surface appearance, we used stimuli that lacked shape and saturated color information. We then investigated the relationship between these perceptual qualities and the various types of image representation through psychophysical experiments. Our results showed that the representation method of some materials affected their perceptual qualities. These cases could be classified into the following three types: (1) perceptual qualities decreased by reproducing the materials as images, (2) perceptual qualities decreased by creating gray images, and (3) perceptual qualities such as "Hardness" and "Coldness" tended to increase when the materials were reproduced as low-quality images. Through methods such as principal component analysis and k-means clustering, we found that material categories are more likely to be confused when materials are represented as images, especially gray images.

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Section: Introductionsupporting

confidence: 63%

Investigating perceptual qualities of static surface appearance using real materials and displayed images

Tanaka

Horiuchi

2015

Vision Research

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“…One might however consider this method to be susceptible to the influence of response bias arising from incomplete/inappropriate instructions or prior knowledge of the participants for the stimuli or task. To compensate for this potential problem, in the last experiment, we used the maximum likelihood difference scaling method (MLDS: Maloney & Yang, 2003), which has been used as a rigorous psychophysical scaling method in many recent studies (Charrier et al, 2007;Devinck et al, 2014;Emrith et al, 2010;Fleming, Jäkel, & Maloney, 2011;Obein, Knoblauch, & Viénot, 2004). Here we estimated a psychophysical scale for perceived liquidness as a function of the discrete Laplacians of image motion vectors.…”

Section: Resultsmentioning

confidence: 99%

“…Over the last decade, the question of how humans perceive materials has received increasing attention (Adelson, 2001;Fleming, 2014;Fleming, Dror, & Adelson, 2003;Fleming, Jäkel, & Maloney, 2011;Kim, Marlow, & Anderson, 2012;Motoyoshi et al, 2007;Nishida & Shinya, 1998;Zaidi, 2011). While previous study of material perception has mainly considered solid materials, many materials around us are in the form of a liquid.…”

Section: Introductionmentioning

confidence: 99%

Seeing liquids from visual motion

et al. 2015

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Most research on human visual recognition focuses on solid objects, whose identity is defined primarily by shape. In daily life, however, we often encounter materials that have no specific form, including liquids whose shape changes dynamically over time. Here we show that human observers can recognize liquids and their viscosities solely from image motion information. Using a two-dimensional array of noise patches, we presented observers with motion vector fields derived from diverse computer rendered scenes of liquid flow. Our observers perceived liquid-like materials in the noise-based motion fields, and could judge the simulated viscosity with surprising accuracy, given total absence of non-motion information including form. We find that the critical feature for apparent liquid viscosity is local motion speed, whereas for the impression of liquidness, image statistics related to spatial smoothness-including the mean discrete Laplacian of motion vectors-is important. Our results show the brain exploits a wide range of motion statistics to identify non-solid materials.

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“…When a transparent layer has a refractive index >1, the background image optically deforms in accordance with the 3D shape of the layer surface. The image deformation due to refraction by itself is considered an ineffective cue to the perception of a transparent layer (5), although the magnitude of the deformation could be a cue to the perception of the thickness of a transparent layer (6). Previous studies have examined the effect of the deformation cue only in static images, however.…”

mentioning

confidence: 91%

Perceptual transparency from image deformation

Kawabe

Maruya

Nishida

2015

Proc. Natl. Acad. Sci. U.S.A.

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Human vision has a remarkable ability to perceive two layers at the same retinal locations, a transparent layer in front of a background surface. Critical image cues to perceptual transparency, studied extensively in the past, are changes in luminance or color that could be caused by light absorptions and reflections by the front layer, but such image changes may not be clearly visible when the front layer consists of a pure transparent material such as water. Our daily experiences with transparent materials of this kind suggest that an alternative potential cue of visual transparency is image deformations of a background pattern caused by light refraction. Although previous studies have indicated that these image deformations, at least static ones, play little role in perceptual transparency, here we show that dynamic image deformations of the background pattern, which could be produced by light refraction on a moving liquid’s surface, can produce a vivid impression of a transparent liquid layer without the aid of any other visual cues as to the presence of a transparent layer. Furthermore, a transparent liquid layer perceptually emerges even from a randomly generated dynamic image deformation as long as it is similar to real liquid deformations in its spatiotemporal frequency profile. Our findings indicate that the brain can perceptually infer the presence of “invisible” transparent liquids by analyzing the spatiotemporal structure of dynamic image deformation, for which it uses a relatively simple computation that does not require high-level knowledge about the detailed physics of liquid deformation.

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Visual Perception of Thick Transparent Materials

Cited by 94 publications

References 23 publications

Investigating perceptual qualities of static surface appearance using real materials and displayed images

Investigating perceptual qualities of static surface appearance using real materials and displayed images

Seeing liquids from visual motion

Perceptual transparency from image deformation

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