GRUFF-3: Generalizing the domain of a function-based recognition system

摘要

Representation systems which support “generic” object recognition offer promising advantages over current model-based vision. Systems applying function-based reasoning are one such approach. In this approach, specific geometric or structural models are disregarded, in favor of analyzing the shape to determine functional requirements for category membership. This paper presents an explanation of the ideas behind function-based modeling and a description of the extensions made to create the Generic Representation Using Form and Function-3 (GRUFF-3) system. This system analyzes the 3D shape of an object and classifies the object according to its possible function as some (sub) category of the superordinate category dishes. The initial GRUFF system implementation was restricted to the furniture domain and required five knowledge primitives (clearance, relative orientation, proximity, dimensions and stability) to realize the functional requirements of the categories represented. The important contribution of our current work is that a significantly larger domain of objects can now be recognized with the addition of just one new knowledge primitive, enclosure. An evaluation of the performance of the system is presented for a database of over 200 3D shapes.