Title :
Adaptive resonance theory networks in the Encephalon autonomous vision system
Author :
Caudell, Thomas P. ; Healy, Michael J.
Author_Institution :
Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A complex neural architecture called the Encephalon is presented as an example of a network that makes extensive use of adaptive resonance theory (ART) networks. The Encephalon is a machine vision system that autonomously learns object classification inference rules, and makes extensive use of the interplay between the bottom-up and top-down flow of information. This paper describes the components of the Encephalon and presents preliminary simulation results
Keywords :
ART neural nets; computer vision; image classification; inference mechanisms; learning (artificial intelligence); ART neural nets; Encephalon; adaptive resonance theory networks; autonomous vision system; bottom-up information flow; machine vision; object classification inference rule learning; top-down information flow; Adaptive systems; Computer networks; Computer vision; Intelligent networks; Laser radar; Machine vision; Neural networks; Resonance; Sensor fusion; Sensor systems;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374362
