Vehicle recognition in infrared images using shared weight neural networks
article
The possibility of using certain kinds of neural networks,
shared weight neural networks, to recognize vehicles in IR images is
investigated. We use the neural network as a preprocessor to find tank
wheels in the images, followed by a more specific traditional search in
the region(s) of interest found by the network; i.e., the neural network is
used as a feature detector. In this way, although we train the networks
on individual samples, we use the trained networks as nonlinear filters
on entire images. The resulting system is tested on scale dependency
and sensitivity to clutter in the background of images and performs
adequately
shared weight neural networks, to recognize vehicles in IR images is
investigated. We use the neural network as a preprocessor to find tank
wheels in the images, followed by a more specific traditional search in
the region(s) of interest found by the network; i.e., the neural network is
used as a feature detector. In this way, although we train the networks
on individual samples, we use the trained networks as nonlinear filters
on entire images. The resulting system is tested on scale dependency
and sensitivity to clutter in the background of images and performs
adequately
TNO Identifier
95063
Source
Optical Engineering, 37(March), pp. 847-857.
Pages
847-857
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