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Proceedings Paper

Performance of Hopfield networks for object recognition in multicontext scenery
Author(s): Jung H. Kim; Sung H. Yoon; Evi H. Park; Celestine A. Ntuen; Shiu M. Cheung; Wagih H. Makky
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Paper Abstract

Nowadays the method for the recognition of partially occluded objects has been needed increasingly. It can be used for airport security such as baggage inspection. Basically algorithm for airport security problem should be fast and exact to get solutions. That is, it should get global optimum as fast as possible. This is why we seek for Annealed Hopfield Network (AHN). Even if AHN is slower than Hybrid Hopfield Network (HHN), AHN provides nearly global solutions without initial restrictions and leads false matching less than HHN. Conclusively it is turned out that AHN is robust to identify occluded target objects with large tolerance of their features.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172513
Show Author Affiliations
Jung H. Kim, North Carolina A&T State Univ. (United States)
Sung H. Yoon, North Carolina A&T State Univ. (United States)
Evi H. Park, North Carolina A&T State Univ. (United States)
Celestine A. Ntuen, North Carolina A&T State Univ. (United States)
Shiu M. Cheung, FAA Technical Ctr. (United States)
Wagih H. Makky, 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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