CFART: a multi-resolutional adaptive resonance system

Hai Lung Hung*, Hong Yuan Mark Liao, Chwen Jye Sze, Shing Jong Lin, Wei Chung Lin, Kuo Chin Fan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, a cascade fuzzy ART (CFART) network is developed and applied to 3D object recognition. The proposed CFART network contains multiple layers which can express a hierarchical representation of an input pattern. The learning processes of the proposed network are unsupervised and self-organizing, which include a top-down searching process and a bottom-up learning process. The proposed CFART can accept both binary and analog inputs. With fast learning and categorization capabilities, the proposed network is capable of acting as an extensible database and providing a multi-resolutional representation of 3D objects. In the experiments, we use superquadrics as a demonstrating example.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages1312-1317
Number of pages6
Volume2
StatePublished - Jan 1 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: Jun 3 1996Jun 6 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period6/3/966/6/96

ASJC Scopus subject areas

  • Software

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