Perceiving animacy from shape

Superordinate visual classification—for example, identifying an image as “animal,” “plant,” or “mineral”—is computationally challenging because radically different items (e.g., “octopus,” “dog”) must be grouped into a common class (“animal”). It is plausible that learning superordinate categories teaches us not only the membership of particular (familiar) items, but also general features that are shared across class members, aiding us in classifying novel (unfamiliar) items. Here, we investigated visual shape features associated with animate and inanimate classes. One group of participants viewed images of 75 unfamiliar and atypical items and provided separate ratings of how much each image looked like an animal, plant, and mineral. Results show systematic tradeoffs between the ratings, indicating a class-like organization of items. A second group rated each image in terms of 22 midlevel shape features (e.g., “symmetrical,” “curved”). The results confirm that superordinate classes are associated with particular shape features (e.g., “animals” generally have high “symmetry” ratings). Moreover, linear discriminant analysis based on the 22-D feature vectors predicts the perceived classes approximately as well as the ground truth classification. This suggests that a generic set of midlevel visual shape features forms the basis for superordinate classification of novel objects along the animacy continuum

The perception of hazy gloss

Most previous work on gloss perception has examined the strength and sharpness of specular reflections in simple bidirectional reflectance distribution functions (BRDFs) having a single specular component. However, BRDFs can be substantially more complex and it is interesting to ask how many additional perceptual dimensions there could be in the visual representation of surface reflectance qualities. To address this, we tested materials with two specular components that elicit an impression of hazy gloss. Stimuli were renderings of irregularly shaped objects under environment illumination, with either a single Ward specular BRDF component (Ward, 1992), or two such components, with the same total specular reflectance but different sharpness parameters, yielding both sharp and blurry highlights simultaneously. Differently shaped objects were presented side by side in matching, discrimination, and rating tasks. Our results show that observers mainly attend to the sharpest reflections in matching tasks, but they can indeed discriminate between single-component and two-component specular materials in discrimination and rating tasks. The results reveal an additional perceptual dimension of gloss-beyond strength and sharpness-akin to ''haze gloss'' (Hunter & Harold, 1987). However, neither the physical measurements of Hunter and Harold nor the kurtosis of the specular term predict perception in our tasks. We suggest the visual system may use a decomposition of specular reflections in the perception of hazy gloss, and we compare two possible candidates: a physical representation made of two gloss components, and an alternative representation made of a central gloss component and a surrounding halo component.Perceptual Representation of Illumination, Shape and Materia

Determining visual shape features for novel object classes

The visual representation of shape reduces a high-dimensional input into a smaller set of more informative features. These features can span a range of abstractions from shallow features based on statistical summaries of images, to deep features related to the generative causes of the shapes. Here we examined the depth of the visual system’s representation of shape by comparing human judgments of whether novel shapes appeared to belong to a common class with a range of models with different shape representations. Each shape class was based on a unique 2D base shape, formed by attaching parts of contours from different naturalistic shapes. We generated novel samples by transforming the base shape’s skeletal representation (Feldman and Singh, 2006) to produce new shapes with limbs that varied in length, width, spatial position, and orientation relative to the base shape. Multiple related classes were derived from each base shape using different distributions of parameter values. On each trial, observers judged whether the given target shape was in the same class as the given context shapes(either one or sixteen samples drawn from a particular shape class). Target shapes were samples taken from the same shape class as the context or one of the 5 related classes. Participants perform remarkably well given the ill-posed nature of the task. Models based on shallow features (Euclidean distance and shape area), and deep features (an ideal observer model with knowledge on the distribution of skeletal parts), were evaluated in terms of trial-by-trial consistency with the human data. In general, human responses indicated generalization beyond the context class and were best described by ideal and sub-optimal observer models suggesting that shape features for novel object classes are an abstract version of the underlying deep features

Perception of physical stability and center of mass of 3-D objects

Humans can judge from vision alone whether an object is physically stable or not. Such judgments allow observers to predict the physical behavior of objects, and hence to guide their motor actions. We investigated the visual estimation of physical stability of 3-D objects (shown in stereoscopically viewed rendered scenes) and how it relates to visual estimates of their center of mass (COM). In Experiment 1, observers viewed an object near the edge of a table and adjusted its tilt to the perceived critical angle, i.e., the tilt angle at which the object was seen as equally likely to fall or return to its upright stable position. In Experiment 2, observers visually localized the COM of the same set of objects. In both experiments, observers´ settings were compared to physical predictions based on the objects´ geometry. In both tasks, deviations from physical predictions were, on average, relatively small. More detailed analyses of individual observers´ settings in the two tasks, however, revealed mutual inconsistencies between observers´ critical-angle and COM settings. The results suggest that observers did not use their COM estimates in a physically correct manner when making visual judgments of physical stability

Key characteristics of specular stereo.

Because specular reflection is view-dependent, shiny surfaces behave radically differently from matte, textured surfaces when viewed with two eyes. As a result, specular reflections pose substantial problems for binocular stereopsis. Here we use a combination of computer graphics and geometrical analysis to characterize the key respects in which specular stereo differs from standard stereo, to identify how and why the human visual system fails to reconstruct depths correctly from specular reflections. We describe rendering of stereoscopic images of specular surfaces in which the disparity information can be varied parametrically and independently of monocular appearance. Using the generated surfaces and images, we explain how stereo correspondence can be established with known and unknown surface geometry. We show that even with known geometry, stereo matching for specular surfaces is nontrivial because points in one eye may have zero, one, or multiple matches in the other eye. Matching features typically yield skew (nonintersecting) rays, leading to substantial ortho-epipolar components to the disparities, which makes deriving depth values from matches nontrivial. We suggest that the human visual system may base its depth estimates solely on the epipolar components of disparities while treating the ortho-epipolar components as a measure of the underlying reliability of the disparity signals. Reconstructing virtual surfaces according to these principles reveals that they are piece-wise smooth with very large discontinuities close to inflection points on the physical surface. Together, these distinctive characteristics lead to cues that the visual system could use to diagnose specular reflections from binocular information.The work was funded by the Wellcome Trust (grants 08459/Z/07/Z & 095183/Z/10/Z) and the EU Marie Curie Initial Training Network “PRISM” (FP7-PEOPLE-2012-ITN, Agreement: 316746).This is the author accepted manuscript. The final version is available from ARVO via http://dx.doi.org/10.1167/14.14.1