'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 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.