The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.

Date Published: 29 March 1989
PDF: 8 pages
Proc. SPIE 1076, Image Understanding and the Man-Machine Interface II, (29 March 1989); doi: 10.1117/12.952677

Published in SPIE Proceedings Vol. 1076:
Image Understanding and the Man-Machine Interface II
Eamon B. Barrett; James J. Pearson, Editor(s)