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Proceedings Paper

Hybrid architecture for KIMS object recognition in a multicontext scene
Author(s): Celestine A. Ntuen; Evi H. Park; Jung H. Kim; Shiu M. Cheung
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Paper Abstract

In a multicontext scene where several objects may be occluded or scenes may change rapidly, a single paradigm for computer vision may not be sufficient. The demand to adjust and learn new environment is therefore a challenging modeling problem in computer vision research. In response to this challenge we have developed a hybrid architecture which combines classical pattern recognition algorithms with fuzzy knowledge-base and Hopfield Neural Network. We also present elementary results obtained from this effort.

Paper Details

Date Published: 1 February 1994
PDF: 9 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172537
Show Author Affiliations
Celestine A. Ntuen, North Carolina A&T State Univ. (United States)
Evi H. Park, North Carolina A&T State Univ. (United States)
Jung H. Kim, North Carolina A&T State Univ. (United States)
Shiu M. Cheung, FAA Technical Ctr. (United States)

Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike M.D.; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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