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

Author Affiliations +

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE