Abstract

Functionality-based object recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function of the category. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value for a primitive evaluation of a particular physical property of an object. In previous versions of a recognition system known as GRUFF, the membership function for each of the primitive evaluations had to be hand-crafted by the system designer. In this paper, we provide a learning component for the GRUFF system, called Omlet, that automatically learns primitive evaluation membership functions given a set of example objects labeled with their desired category measure. T...

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