Function-Based Object Recognition

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. 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 di erent properties of the object's shape. A membershipfunction is used to compute the membershipvalue when evaluating a primitive 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 was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally app...

• Type: Conference
• Year: 1994
• Where: DAGSTUHL
• Authors: Louise Stark, Kevin W. Bowyer
• Keywords: DAGSTUHL 1994, DAGSTUHL 2007, Membership Value, Primitive Evaluations, Systems Recognize Objects