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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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