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

This paper is a study of techniques for measuring and predicting visual fidelity. As visual stimuli we use polygonal models, and vary their fidelity with two different model simplification algorithms. We also group the stimuli into two object types: animals and man made artifacts. We examine three different experimental techniques for measuring these fidelity changes: naming times, ratings, and preferences. All the measures were sensitive to the type of simplification and level of simplification. However, the measures differed from one another in their response to object type. We also examine several automatic techniques for predicting these experimental measures, including techniques based on images and on the models themselves. Automatic measures of fidelity were successful at predicting experimental ratings, less successful at predicting preferences, and largely failures at predicting naming times. We conclude with suggestions for use and improvement of the experimental and automatic measures of visual fidelity.

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.

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)

Recent evidence suggests that scene recognition across views is impaired when an array of objects rotates relative to a stationary observer, but not when the observer moves relative to a stationary display [Simons, D.J., Wang, R.F., 1998. Perceiving real-world viewpoint changes. Psychological Science 9, 315–320]. The experiments in this report examine whether the relatively poorer performance by stationary observers across view changes results from a lack of perceptual information for the rotation or from the lack of active control of the perspective change, both of which are present for viewpoint changes. Three experiments compared performance when observers passively experienced the view change and when they actively caused the change. Even with visual information and active control over the display rotation, change detection performance was still worse for orientation changes than for viewpoint changes. These findings suggest that observers can update a viewer-centered representation of a scene when they move to a different viewing position, but such updating does not occur during display rotations even with visual and motor information for the magnitude of the change. This experimental approach, using arrays of real objects rather

Model simplification researchers require quality heuristics to guide simplification, and quality predictors to allow comparison of different simplification algorithms. However, there has been little evaluation of these heuristics or predictors. We present an evaluation of quality predictors. Our standard of comparison is naming time, a well established measure of recognition from cognitive psychology. Thirty participants named models of familiar objects at three levels of simplification. Results confirm that naming time is sensitive to model simplification. Correlations indicate that view-dependent image quality predictors are most effective for drastic simplifications, while view-independent three-dimensional predictors are better for more moderate simplifications. Keywords Model simplification, simplification metrics, image quality, naming time, human vision. INTRODUCTION As the number of methods available for constructing or capturing three dimensional (3D) polygonal models prol...

At a given instant we see only visible surfaces, not an object's complete 3D appearance. Thus, objects may be represented as discrete `views' showing only those features visible from a limited range of viewpoints. We address how to define a view using Koenderink's (Koenderink & Van Doorn, Biol. Cybernet. 32 (1979) 211.) geometric method for enumerating complete sets of stable views as aspect graphs. Using objects with known aspect graphs, five experiments examined whether the perception of orientation is sensitive to the qualitative features that define aspect graphs. Highest sensitivity to viewpoint changes was observed at locations where the theory predicts qualitative transitions, although some transitions did not affect performance. Hypotheses about why humans ignore some transitions offer insights into mechanisms for object representation. 2001 Elsevier Science Ltd. All rights reserved.

Previous studies suggest that there are "canonical" viewpoints from which objects are identified most readily. Object naming has been the primary objective measure used to assess view canonicality, but this task has not proven adequate in distinguishing the many explanations of this phenomenon that have been offered. We examine object naming (Experiment 1a) and name verification (Experiments 1b and 2) to better understand the origin and nature of canonical view effects in recognition. In the name verification experiments, observers read an object name and then viewed an image of an object and decided as quickly as possible whether the image matched the name. The stimuli were images of 3D computer models of seven common objects. Each object was rendered from one canonical viewpoint (determined in a separate experimentby Blanz, Tarr, Bülthoff, & Vetter, 1996) and two noncanonical viewpoints. Observers named the objects faster in canonical views, but performance was not affected by viewpoint...