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Proceedings Paper

Recognition of partially occluded threat objects based on the annealed Hopfield network
Author(s): Jung H. Kim; Sung H. Yoon; Evi H. Park; Celestine A. Ntuen; Shiu M. Cheung
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Paper Abstract

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybrid Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a Neural Network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from X-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

Paper Details

Date Published: 1 April 1993
PDF: 9 pages
Proc. SPIE 1824, Applications of Signal and Image Processing in Explosives Detection Systems, (1 April 1993); doi: 10.1117/12.142895
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)


Published in SPIE Proceedings Vol. 1824:
Applications of Signal and Image Processing in Explosives Detection Systems
James M. Connelly; Shiu M. Cheung, Editor(s)

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