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Proceedings Paper

Cascade fuzzy ART: a new extensible database for model-based object recognition
Author(s): Hai-Lung Hung; Hong-Yuan Mark Liao; Shing-Jong Lin; Wei-Chung Lin; Kuo-Chin Fan
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Paper Abstract

In this paper, we propose a cascade fuzzy ART (CFART) neural network which can be used as an extensible database in a model-based object recognition system. The proposed CFART networks can accept both binary and continuous inputs. Besides, it preserves the prominent characteristics of a fuzzy ART network and extends the fuzzy ART's capability toward a hierarchical class representation of input patterns. The learning processes of the proposed network are unsupervised and self-organizing, which include coupled top-down searching and bottom-up learning processes. In addition, a global searching tree is built to speed up the learning and recognition processes.

Paper Details

Date Published: 27 February 1996
PDF: 12 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233231
Show Author Affiliations
Hai-Lung Hung, Northwestern Univ. (United States)
Hong-Yuan Mark Liao, Institute of Information Science (Taiwan)
Shing-Jong Lin, National Central Univ. (Taiwan)
Wei-Chung Lin, Northwestern Univ. (United States)
Kuo-Chin Fan, National Central Univ. (Taiwan)

Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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