Performance of Hopfield networks for object recognition in multicontext scenery

Jung H. Kim; Sung H. Yoon; Evi H. Park; Celestine A. Ntuen; Shiu M. Cheung; Wagih H. Makky

doi:10.1117/12.172513

1 February 1994 Performance of Hopfield networks for object recognition in multicontext scenery

Jung H. Kim, Sung H. Yoon, Evi H. Park, Celestine A. Ntuen, Shiu M. Cheung, Wagih H. Makky

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172513
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

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.

Citation Download Citation

Jung H. Kim, Sung H. Yoon, Evi H. Park, Celestine A. Ntuen, Shiu M. Cheung, and Wagih H. Makky "Performance of Hopfield networks for object recognition in multicontext scenery", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172513

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