Visual Perception Of Materials Research Articles

Shatter and splatter: The contribution of mechanical and optical properties to the perception of soft and hard breaking materials.

Research on the visual perception of materials has mostly focused on the surface qualities of rigid objects. The perception of substance like materials is less explored. Here, we investigated the contribution of, and interaction between, surface optics and mechanical properties to the perception of nonrigid, breaking materials. We created novel animations of materials ranging from soft to hard bodies that broke apart differently when dropped. In Experiment 1, animations were rendered as point-light movies varying in dot density, as well as "full-cue" optical versions ranging from translucent glossy to opaque matte under a natural illumination field. Observers used a scale to rate each substance on different attributes. In Experiment 2 we investigated how much shape contributed to ratings of the full-cue stimuli in Experiment 1, by comparing ratings when observers were shown movies versus one frame of the animation. The results showed that optical and mechanical properties had an interactive effect on ratings of several material attributes. We also found that motion and static cues each provided a lot of information about the material qualities; however, when combined, they influenced observers' ratings interactively. For example, in some conditions, motion dominated over optical information; in other conditions, it enhanced the effect of optics. Our results suggest that rating multiple attributes is an effective way to measure underlying perceptual differences between nonrigid breaking materials, and this study is the first to our knowledge to show interactions between optical and mechanical properties in a task involving judgments of perceptual qualities.

Using Japanese Onomatopoeias to Explore Perceptual Dimensions in Visual Material Perception.

This study examined the perceptual dimensions of visual material properties. Photographs of 50 objects were presented to the participants, and they reported a suitable onomatopoeia (mimetic word) for describing the material of the object in each photograph, based on visual appearance. The participants' responses were collated into a contingency table of photographs × onomatopoeias. After removing some items from the table, correspondence analysis was applied to the contingency table, and a six-dimensional biplot was obtained. By rotating the axes to maximize sparseness of the coordinates for the items in the biplot, three meaningful perceptual dimensions were derived: wetness/stickiness, fluffiness/softness, and smoothness-roughness/gloss-dullness. Two additional possible dimensions were obtained: crumbliness and coldness. These dimensions, except gloss-dullness, were paid little attention to in vision science, though they were suggested as perceptual dimensions of tactile texture. This suggests that the perceptual dimensions that are considered to be primarily related to haptics are also important in visual material perception.

MatMix 1.0, a novel material probe for quantitatively measuring visual perception of materials

MatMix 1.0, a novel material probe for quantitatively measuring visual perception of materials

Visual perception of materials and their properties

Misidentifying materials-such as mistaking soap for pâté, or vice versa-could lead to some pretty messy mishaps. Fortunately, we rarely suffer such indignities, thanks largely to our outstanding ability to recognize materials-and identify their properties-by sight. In everyday life, we encounter an enormous variety of materials, which we usually distinguish effortlessly and without error. However, despite its subjective ease, material perception poses the visual system with some unique and significant challenges, because a given material can take on many different appearances depending on the lighting, viewpoint and shape. Here, I use observations from recent research on material perception to outline a general theory of material perception, in which I suggest that the visual system does not actually estimate physical parameters of materials and objects. Instead-I argue-the brain is remarkably adept at building 'statistical generative models' that capture the natural degrees of variation in appearance between samples. For example, when determining perceived glossiness, the brain does not estimate parameters of the BRDF. Instead, it uses a constellation of low- and mid-level image measurements to characterize the extent to which the surface manifests specular reflections. I argue that these 'statistical appearance models' are both more expressive and easier to compute than physical parameters, and therefore represent a powerful middle way between a 'bag of tricks' and 'inverse optics'.

Visual perception of materials and surfaces

Summary The visual system relies on patterns of light to provide information about the layout of objects that populate our environment. Light is structured by the way it interacts with the three-dimensional shape, reflectance, and transmittance properties of objects. The input for vision is therefore a complex, conflated mixture of different sources of physical variation that the brain must somehow disentangle to recover the intrinsic properties of the objects and materials that fill the world.