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"We do dishes, but we don't do windows": Function-based modeling and recognition of rigid objects
Conference proceeding

"We do dishes, but we don't do windows": Function-based modeling and recognition of rigid objects

Melanie Sutton, Louise Stark and Bowyer Kevin
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision: Applications in Optical Science and Engineering, 1992, Boston, MA, United States, pp.132-143
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision: Applications in Optical Science and Engineering (Boston, MA, United States)
1992
DOI: https://doi.org/10.1117/12.131522
Web of Science ID: WOS:A1992BX27F00012

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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 arid 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 advantage of the function-based approach over the traditional model-based approach would be lost. This paper presents some initial experimental results from the GIUJFF-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.

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