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Proceedings Paper

'We do dishes, but we don't do windows': function-based modeling and recognition of rigid objects
Author(s): Melanie A. Sutton; Louise Stark; Kevin W. Bowyer
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Paper Abstract

Generic recognition for computer vision is a goal that is still far from reality. Part of the problem rests in the inherent limitations of current `model-based' vision. Our approach moves away from specific geometric or structural models and instead focuses on the functionality of the object as the property which drives the recognition process. This results in a representation that is generic in the sense of capturing an entire category of objects. One important assumption underlying the form and function approach is that a `small' number of `primitive' concepts about shape, physics, and causation will suffice to define the functionality of a broad range of categories. If multiple new `primitives' were required to define each additional category, then much of the advantages of the function-based approach over the traditional model-based approach would be lost. This paper presents some initial experimental results from the GRUFF-3 system, which uses function-based representation to recognize rigid objects in the superordinate category dishes. The performance of this system has been evaluated on a database of approximately 200 shapes.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131522
Show Author Affiliations
Melanie A. Sutton, Univ. of South Florida (United States)
Louise Stark, Univ. of South Florida (United States)
Kevin W. Bowyer, Univ. of South Florida (United States)

Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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