The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N ^ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermmation. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configura-tions to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimen-sional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but in-stead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its

Is human object recognition viewpoint dependent or viewpointinvariant under #everyday " conditions? Biederman and Gerhardstein #1993# argue that viewpoint-invariant mechanisms are used almost exclusively.However, our analysis indicates that: 1# their conditions for immediate viewpointinvariance lack the generalitytocharacterize a wide range of recognition phenomena; 2# the extensive body of viewpoint-dependent results cannot be dismissed as processing #by-products" or #experimental artifacts"; 3# geon structural descriptions cannot coherently account for category recognition, the domain they are intended to explain. We conclude that the weight of current evidence supports an exemplar-based multiple-views mechanism as an important component of both exemplar-speci#c and categorical recognition. # Many of the ideas in this paper were developed during visits by MJT to the Max#Planck#Institut f#ur biologische Kybernetik in T#ubingen, Germany.We thank Dan Kersten for his insightful comments...

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Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for `image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with `structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, as well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural-description theories. 1998 Elsevier Science B.V. All rights reserved Keywords: Object recognition; Image-based model; Structural description 1.

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the openended character both of natural kinds and of artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readil...

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The use of outline shape in recognizing objects was investigated in four experiments. In Experiment 1, subjects matched a shaded image to either another shaded image or a silhouette. In Experiment 2, subjects initially named shaded images; later they named either shaded images or silhouettes. Performance in both experiments was predicted by changes in the outline shape of the stimuli. The same matching (Experiment 3) and priming (Experiment 4) paradigms were then used to investigate recognition with objects that were rotated between presentations so as to change the outline shape of the object. Recognition was predicted by changes to outline shape. These results place constraints on models of object recognition, and are most compatible with viewpoint-dependent models of recognition.

this paper is to investigate the nature of image-based class generalization,

Categories in the middle level of a taxonomic hierarchy tend to be highly differentiated in that they have both high levels of within-category similarity and low levels of between-category similarity. Research on similarity reveals a distinction between pairs of categories that are seen as dissimilar because they have few commonalities and pairs that are seen as dissimilar because they have many psychologically relevant alignable differences. The authors suggest that the low between-category similarity proposed for neighboring basic-level categories is actually a matter of having many psychologically relevant differences. In contrast, the low between-category similarity of superordinates is a result of their having few commonalities. The authors evaluate this claim in 4 experiments using a variety of natural stimuli and converging measures. The data support the importance of aliguable differences for distinguish-ing between pairs of basic-level categories. People typically categorize objects at a number of levels of generality. For example, an object on top of a coffee table that has a rectangular shape, contains pages of printed text, and describes a mysterious murder can be called a murder

A host of experimental data has been accumulating on the properties and mechanisms of object recognition in cortex. We review the main findings, and summarize them using a quantitative, biologically plausible, Standard Model. The model is a tool to interpret and understand the available data, and generate questions and predictions for new experiments.