What makes viewpoint-invariant properties perceptually salient?

Perceptual Grouping Explains Similarities in Constellations Across Cultures

Cultures around the world organize stars into constellations, or asterisms, and these groupings are often considered to be arbitrary and culture specific. Yet there are striking similarities in asterisms across cultures, and groupings such as Orion, the Big Dipper, the Pleiades, and the Southern Cross are widely recognized across many different cultures. Psychologists have informally suggested that these shared patterns are explained by Gestalt laws of grouping, but there have been no systematic attempts to catalog asterisms that recur across cultures or to explain the perceptual basis of these groupings. Here, we compiled data from 27 cultures around the world and found that a simple computational model of perceptual grouping accounts for many of the recurring cross-cultural asterisms. Our results suggest that basic perceptual principles account for more of the structure of asterisms across cultures than previously acknowledged and highlight ways in which specific cultures depart from this shared baseline.

Lunar Crater Identification in Digital Images

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization

In 1912, Max Wertheimer published his paper on phi motion, widely recognized as the start of Gestalt psychology. Because of its continued relevance in modern psychology, this centennial anniversary is an excellent opportunity to take stock of what Gestalt psychology has offered and how it has changed since its inception. We first introduce the key findings and ideas in the Berlin school of Gestalt psychology, and then briefly sketch its development, rise, and fall. Next, we discuss its empirical and conceptual problems, and indicate how they are addressed in contemporary research on perceptual grouping and figure-ground organization. In particular, we review the principles of grouping, both classical (e.g., proximity, similarity, common fate, good continuation, closure, symmetry, parallelism) and new (e.g., synchrony, common region, element and uniform connectedness), and their role in contour integration and completion. We then review classic and new image-based principles of figure-ground organization, how it is influenced by past experience and attention, and how it relates to shape and depth perception. After an integrated review of the neural mechanisms involved in contour grouping, border ownership, and figure-ground perception, we conclude by evaluating what modern vision science has offered compared to traditional Gestalt psychology, whether we can speak of a Gestalt revival, and where the remaining limitations and challenges lie. A better integration of this research tradition with the rest of vision science requires further progress regarding the conceptual and theoretical foundations of the Gestalt approach, which is the focus of a second review article.

Sensitivity to nonaccidental properties across various shape dimensions

Nonaccidental properties (NAPs) are image properties that are invariant over orientation in depth and are distinguished from metric properties (MPs) that can change continuously with variations over depth orientation. To a large extent NAPs allow facile recognition of objects at novel viewpoints. Two match-to-sample experiments with 2D or 3D appearing geons assessed sensitivity to NAP vs. MP differences. A matching geon was always identical to the sample and the distractor differed from the matching geon in either a NAP or an MP on a single generalized cone dimension. For example, if the sample was a cylinder with a slightly curved axis, the NAP distractor would have a straight axis and the MP distractor would have an axis of greater curvature than the sample. Critically, the NAP and MP differences were scaled so that the MP differences were slightly greater according to pixel energy and Gabor wavelet measures of dissimilarity. Exp. 1 used a staircase procedure to determine the threshold presentation time required to achieve 75% accuracy. Exp. 2 used a constant, brief display presentation time with reaction times and error rates as dependent measures. Both experiments revealed markedly greater sensitivity to NAP over MP differences, and this was generally true for the individual dimensions. The NAP advantage was not reflected in the similarity computations of the C2 stage of HMAX, a widely cited model of later stage cortical ventral stream processing.

Outdoor scene image segmentation based on background recognition and perceptual organization

In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.

Using geometry to specify location: implications for spatial coding in children and nonhuman animals

The study of spatial cognition has benefited greatly from a technique known as the disorientation procedure. This procedure was originally used with rats to show that they relied on the geometry of an enclosed space to locate a target hidden in that space. Disorientation has since been used with a variety of mobile animals, including human children, to examine the coding of geometric information. Here, we focus mostly on our recent work with young children. We examine a set of issues concerning reorientation--namely, the nature of geometric coding, the processes invoked by disorientation, and the developmental origins of using geometric information to determine location. We have employed a variety of methods to examine these issues; the methods include analyzing search behaviors, using spaces of different shapes, varying viewing position, and comparing different disorientation procedures. The implications for how children and nonhuman animals code geometric information are discussed.

Encoding Multielement Scenes: Statistical Learning of Visual Feature Hierarchies

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.