Hybrid architecture for KIMS object recognition in a multicontext scene

Celestine A. Ntuen; Evi H. Park; Jung H. Kim; Shiu M. Cheung

doi:10.1117/12.172537

1 February 1994 Hybrid architecture for KIMS object recognition in a multicontext scene

Celestine A. Ntuen, Evi H. Park, Jung H. Kim, Shiu M. Cheung

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172537
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

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.

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Celestine A. Ntuen, Evi H. Park, Jung H. Kim, and Shiu M. Cheung "Hybrid architecture for KIMS object recognition in a multicontext scene", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172537

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