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http://dx.doi.org/10.7838/jsebs.2021.26.2.083

Annealed Hopfield Neural Network for Recognizing Partially Occluded Objects  

Yoon, Suk-Hun (Department of Industrial and Information Systems Engineering, Soongsil University)
Publication Information
The Journal of Society for e-Business Studies / v.26, no.2, 2021 , pp. 83-94 More about this Journal
Abstract
The need for recognition of partially occluded objects is increasing in the area of computer vision applications. Occlusion causes significant problems in identifying and locating an object. In this paper, an annealed Hopfield network (AHN) is proposed for detecting threat objects in passengers' check-in baggage. AHN is a deterministic approximation that is based on the hybrid Hopfield network (HHN) and annealing theory. AHN uses boundary features composed of boundary points and corner points which are extracted from input images of threat objects. The critical temperature also is examined to reduce the run time of AHN. Extensive computational experiments have been conducted to compare the performance of the AHNwith that of the HHN.
Keywords
Hopfield Network; Partially Occluded Objects; Annealing; Recognition;
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