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

Volume

2

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1235

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICNN.1994.374362

Filename

374362