Abstract
Representation schemes traditionally used in model-based vision are contrasted with the “function-based” representation scheme. A system which utilizes function-based representation has been implemented and tested, using the object category “chair” for case study. Function-based description is used to recognize classes and identify subclasses of known categories of objects, even if the specific object has never been encountered previously. Interpretation of the functionality of an object is accomplished through qualitative reasoning about its 3-D shape. During the recognition process, evidence is gathered as to how well the functional requirements are satisfied by the input shape. An investigation of different types of operators used in the combination of the functional evidence has been made. Three pairs of conjunctive and disjunctive operators have been used in the recognition process of more than 100 object shapes. The results are compared and differences are discussed.
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