Discerning nonrigid 3D shapes from motion cues

Jain, Anshul; Zaidi, Qasim

doi:10.1073/pnas.1016211108

Cited by 39 publications

(33 citation statements)

References 69 publications

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“…Interestingly, Todd noted that at intermediate levels of correspondence a rigid surface appeared to be “scintillating” [35]. 3D shape reliability might be extracted by neural mechanisms involved in the estimation of both shape and motion from optic flow [36, 37]. …”

Section: Discussionmentioning

confidence: 99%

Visual Motion and the Perception of Surface Material

et al. 2011

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Summary Many critical perceptual judgments, from telling whether fruit is ripe, to determining whether the ground is slippery, involve estimating the material properties of surfaces. Very little is known about how the brain recognizes materials, even though the problem is likely as important for survival as navigating or recognizing objects. Though previous research has focused nearly exclusively on the properties of static images [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15,16], recent evidence suggests that motion may affect the appearance of surface material [17, 18, 19]. However, what kind of information motion conveys and how this information may be used by the brain is still unknown. Here, we have identified three motion cues that the brain could rely on to distinguish between matte and shiny surfaces. We show that these motion measurements can override static cues, leading to dramatic changes in perceived material depending on the image motion characteristics. A classifier algorithm based on these cues correctly predicts both successes and some striking failures of human material perception. Together these results reveal a previously unknown use for optic flow in the perception of surface material properties.

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Section: Discussionmentioning

confidence: 99%

Visual Motion and the Perception of Surface Material

et al. 2011

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“…Previous studies have shown that the human visual system is capable of recognizing the shape and material of nonrigid objects from image motion (24,25). For instance, our recent study (26) found that pure visual motion flows extracted from scenes of running opaque liquids are sufficient for human observers to perceive liquids and their viscosity.…”

Section: Discussionmentioning

confidence: 99%

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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“…Subsequent processing analyzes the spatiotemporal patterns formed by those local motion signals. Certain multi-element motion pattern analyses have been studied extensively, including optic flow (Duffy, 2003;Gibson, 1950;Warren, 2008), structure from motion (Jain & Zaidi, 2011;Ullman, 1979), biological motion (Blake & Shiffrar, 2007;Johansson, 1973;Troje, 2008), and material perception (Doerschner et al, 2011). The basic computational components of motion pattern analysis remain poorly understood, however.…”

Section: Introductionmentioning

confidence: 99%

Rapid encoding of relationships between spatially remote motion signals

2013

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For visual processing, the temporal correlation of remote local motion signals is a strong cue to detect meaningful large-scale structures in the retinal image, because related points are likely to move together regardless of their spatial separation. While the processing of multi-element motion patterns involved in biological motion and optic flow has been studied intensively, the encoding of simpler pairwise relationships between remote motion signals remains poorly understood. We investigated this process by measuring the temporal rate limit for perceiving the relationship of two motion directions presented at the same time at different spatial locations. Compared to luminance or orientation, motion comparison was more rapid. Performance remained very high even when interstimulus separation was increased up to 100°. Motion comparison also remained rapid regardless of whether the two motion directions were similar to or different from each other. The exception was a dramatic slowing when the elements formed an orthogonal "T," in which two motions do not perceptually group together. Motion presented at task-irrelevant positions did not reduce performance, suggesting that the rapid motion comparison could not be ascribed to global optic flow processing. Our findings reveal the existence and unique nature of specialized processing that encodes long-range relationships between motion signals for quick appreciation of global dynamic scene structure.

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Discerning nonrigid 3D shapes from motion cues

Cited by 39 publications

References 69 publications

Visual Motion and the Perception of Surface Material

Visual Motion and the Perception of Surface Material

Perceptual transparency from image deformation

Rapid encoding of relationships between spatially remote motion signals

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