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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In 7 experiments the authors investigated the locus of word frequency effects in speech production. Experiment 1 demonstrated a frequency effect in picture naming that was robust over repetitions. Experiments 2, 3, and 7 excluded contributions from object identification and initiation of articulation. Experiments 4 and 5 investigated whether the effect arises in accessing the syntactic word (lemma) by using a grammatical gender decision task. Although a frequency effect was found, it dissipated under repeated access to a word's gender. Experiment 6 tested whether the robust frequency effect arises in accessing the phonological form (lexeme) by having Ss translate words that produced homophones. Low-frequent homophones behaved like high-frequent controls, inheriting the accessing speed of their high-frequent homophone twins. Because homophones share the lexeme, not the lemma, this suggests a lexeme-level origin of the robust effect. The word frequency effect in speech production was discovered by Oldfield and Wingfield (1965). In a picture-naming task, they found that pictures with low-frequency (LF) names (such as syringe) took longer to name than pictures with high-frequency (HF) names (such as basket). Wingfield (1968)

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,

this report was submitted about how the processes are affected by fre- to fulfill the requirements for a Masters degree from the quency and constraint

Individual faces vary considerably in both the quality and quantity of the information they contain for recognition and for viewpoint generalization. In the present study, we assessed the typicality, recognizability, and viewpoint generalizability of individual faces using data from both human observers and from a computational model of face recognition across viewpoint change. The two-stage computational model incorporated a viewpoint alignment operation and a recognition-by-interpolation operation. An interesting aspect of this particular model is that the effects of typicality it predicts at the alignment and recognition stages dissociate, such that face typicality is beneficial for the success of the alignment process, but is adverse for the success of the recognition process. We applied a factor analysis to the covariance data for the human- and model-derived face measures across the different viewpoints and found two axes that appeared consistently across all viewpoints. Projecti...

Abstract: A number of recent successful models of face recognition posit only two layers, an input layer consisting of a lattice of spatial filters and a single subsequent stage by which those descriptor values are mapped directly onto an object representation layer by standard matching methods such as stochastic optimization. Is this approach sufficient for modeling human object recognition? We tested whether a highly efficient version of such a two-layer model would manifest effects similar to those shown by humans when given the task of recognizing images of objects that had been employed in a series of psychophysical experiments. System accuracy was quite high overall, but was qualitatively different from that evidenced by humans in object recognition tasks. The discrepancy between the system's performance and human performance is likely to be revealed by all models that map filter values directly onto object units. These results suggest that human object recognition (as opposed to face recognition) may be difficult to approximate by models that do not posit hidden units for explicit representation of intermediate entities such as edges, viewpoint invariant classifiers, axes, shocks and/or object parts.

In this report we present a general introduction to object recognition. We begin with brief discussions of the terminology used in the object recognition literature and the psychophysical tasks that are used to investigate object recognition. We then discuss models of shape representation. We dispense with the idea that shape representations are like the 3-D models used in computer aided design and explore instead models of shape representation that are based on feature descriptions. As these descriptions encode only the features that are visible from a particular viewpoint, they are generally viewpoint-specific. We discuss various means of achieving viewpoint-invariant recognition using such descriptions, including reliance on diagnostic features visible from a wide range of viewpoints, storage of multiple descriptions for each object, and the use of transformation mechanisms. Finally, we discuss how differences in viewpoint dependence that are often observed for within-category and b...