Paper
1 November 1992 'We do dishes, but we don't do windows': function-based modeling and recognition of rigid objects
Melanie A. Sutton, Louise Stark, Kevin W. Bowyer
Author Affiliations +
Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992) https://doi.org/10.1117/12.131522
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Melanie A. Sutton, Louise Stark, and Kevin W. Bowyer "'We do dishes, but we don't do windows': function-based modeling and recognition of rigid objects", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131522
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Cited by 3 scholarly publications.
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KEYWORDS
Glasses

Computer vision technology

Machine vision

Shape analysis

Robot vision

Robots

Visual process modeling

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