NASA Technical Reports Server (NTRS) 19930010277: Recognition of pa... | |
by NASA Technical Reports Server (NTRS) | |
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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. The neural network | |
approach is suitable for the problems in the sense that | |
the inherent parallelism of neural networks pursues many | |
hypotheses in parallel resulting in high computation | |
rates. Moreover, they provide a greater degree of | |
robustness or fault tolerance than conventional | |
computers. The annealed Hopfield network which is derived | |
from the mean field annealing (MFA) has been developed to | |
find global solutions of a nonlinear system. In the | |
study, it has been proven that the system temperature of | |
MFA is equivalent to the gain of the sigmoid function of | |
a Hopfield network. In our early work, we developed the | |
hybrid Hopfield network (HHN) for fast and reliable | |
matching. However, HHN doesn't guarantee global solutions | |
and yields false matching under heavily occluded | |
conditions because HHN is dependent on initial states by | |
its nature. 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 | |
hybird 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. | |
Date Published: 2016-09-30 17:59:45 | |
Identifier: NASA_NTRS_Archive_19930010277 | |
Item Size: 11829183 | |
Language: english | |
Media Type: texts | |
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