Neural network paradigm for three-dimensional object recognition
Ryan G. Rosandich, Ci̇han H. Dağli · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The goal of this research is to develop a vision system that is capable of recognizing objects based on past experience. This paper introduces the highest level of this system, which consists of a neural network that is capable of learning to recognize 3D objects. Knowledge about objects is acquired by learning their various views, guises, or aspects. Learning occurs on two levels. First, supervised competitive learning is employed to teach the network to differentiate between different objects. The competition causes the unique differences between objects to be emphasized in this stage. Second, unsupervised cooperative learning is employed to self-organize the various aspects of a given object. This stage works in a manner similar to the ART family of self-organizing networks. The cooperative learning causes similarities between different aspects of the same object to be emphasized. The object recognition system is intended for use in a manufacturing environment, including tasks such as component identification, classification of visible quality defects, and visual product grading and sorting.