Perception of material properties

Conditions of a Multi-View 3D Display for Accurate Reproduction of Perceived Glossiness

Visualizing objects as they are perceived in the real world is often critical in our daily experiences. We previously focused on objects' surface glossiness visualized with a 3D display and found that a multi-view 3D display reproduces perceived glossiness more accurately than a 2D display. This improvement of glossiness reproduction can be explained by the fact that a glossy surface visualized by a multi-view 3D display appropriately provides luminance differences between the two eyes and luminance changes accompanying the viewer's lateral head motion. In the present study, to determine the requirements of a multi-view 3D display for the accurate reproduction of perceived glossiness, we developed a simulator of a multi-view 3D display to independently and simultaneously manipulate the viewpoint interval and the magnitude of the optical inter-view crosstalk. Using the simulator, we conducted a psychophysical experiment and found that glossiness reproduction is most accurate when the viewpoint interval is small and there is just a small (but not too small) amount of crosstalk. We proposed a simple yet perceptually valid model that quantitatively predicts the reproduction accuracy of perceived glossiness.

Human brain activity reflecting facial attractiveness from skin reflection

Facial attraction has a great influence on our daily social interactions. Previous studies have mainly focused on the attraction from facial shape and expression. We recently found that faces with radiant skin appear to be more attractive than those with oily-shiny or matte skin. In the present study, we conducted functional magnetic resonance imaging (fMRI) and psychological experiments to determine the human brain activity that reflects facial attractiveness modulated by these skin reflection types. In the fMRI experiment, female subjects were shown successive images of unfamiliar female faces with matte, oily-shiny, or radiant skin. The subjects compared each face with the immediately preceding face in terms of attractiveness, age, and skin reflection, all based on the skin. The medial part of the orbitofrontal cortex (mOFC) was significantly more active when comparing attractiveness than when comparing skin reflection, suggesting that the mOFC is involved in processing facial attractiveness from skin reflection. In the psychological experiment, attractiveness rating was highest for radiant skin, followed by oily-shiny, and then matte skin. Comparison of the results of these experiments showed that mOFC activation level increased with attractiveness rating. These results suggest that the activation level of the mOFC reflects facial attractiveness from skin reflection.

The joint role of geometry and illumination on material recognition

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks.

Expectations affect the perception of material properties

Many objects that we encounter have typical material qualities: spoons are hard, pillows are soft, and Jell-O dessert is wobbly. Over a lifetime of experiences, strong associations between an object and its typical material properties may be formed, and these associations not only include how glossy, rough, or pink an object is, but also how it behaves under force: we expect knocked over vases to shatter, popped bike tires to deflate, and gooey grilled cheese to hang between two slices of bread when pulled apart. Here we ask how such rich visual priors affect the visual perception of material qualities and present a particularly striking example of expectation violation. In a cue conflict design, we pair computer-rendered familiar objects with surprising material behaviors (a linen curtain shattering, a porcelain teacup wrinkling, etc.) and find that material qualities are not solely estimated from the object's kinematics (i.e., its physical [atypical] motion while shattering, wrinkling, wobbling etc.); rather, material appearance is sometimes “pulled” toward the “native” motion, shape, and optical properties that are associated with this object. Our results, in addition to patterns we find in response time data, suggest that visual priors about materials can set up high-level expectations about complex future states of an object and show how these priors modulate material appearance.

Measuring and Modeling Visual Appearance

In studying visual perception, we seek to develop models of processing that accurately predict perceptual judgments. Much of this work is focused on judgments of discrimination, and there is a large literature concerning models of visual discrimination. There are, however, non-threshold visual judgments, such as judgments of the magnitude of differences between visual stimuli, that provide a means to bridge the gap between threshold and appearance. We describe two such models of suprathreshold judgments, maximum likelihood difference scaling and maximum likelihood conjoint measurement, and review recent literature that has exploited them.

Neural Mechanisms of Material Perception: Quest on Shitsukan

In recent years, a growing body of research has addressed the nature and mechanism of material perception. Material perception entails perceiving and recognizing a material, surface quality or internal state of an object based on sensory stimuli such as visual, tactile, and/or auditory sensations. This process is ongoing in every aspect of daily life. We can, for example, easily distinguish whether an object is made of wood or metal, or whether a surface is rough or smooth. Judging whether the ground is wet or dry or whether a fish is fresh also involves material perception. Information obtained through material perception can be used to govern actions toward objects and to make decisions about whether to approach an object or avoid it. Because the physical processes leading to sensory signals related to material perception is complicated, it has been difficult to manipulate experimental stimuli in a rigorous manner. However, that situation is now changing thanks to advances in technology and knowledge in related fields. In this article, we will review what is currently known about the neural mechanisms responsible for material perception. We will show that cortical areas in the ventral visual pathway are strongly involved in material perception. Our main focus is on vision, but every sensory modality is involved in material perception. Information obtained through different sensory modalities is closely linked in material perception. Such cross-modal processing is another important feature of material perception, and will also be covered in this review.