الفهرس 
Computer imaging surveyOnly 14 pages are availabe for public view
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Abstract
This thesis describes three image recognition models. The
applications on hand utilize the eigenvector technique and the neural
networks for aircraft identification purpose. The methods of image
identification are reviewed using statistical, syntactic, and neural
network. In the first method, the contour of each aircraft image is isolated
and clipped using image pre-processing operations. Image silhouettes
normalization followed by feature extraction are done using the principal
component analysis. A highly recognition success is obtained with
x-features, where x is the number of referenced aircraft.
An aircraft identification system based on back-propagation neural
network is presented. The effect of activation function and number of
hidden neurons on recognition performance is studied through this work.
The last approach is based on Kohonen and Grossberg models. It was
found that the aircraft recognition method based on back-propagation and
counter propagation approaches has been succeeded to recognize the test
images with signal to noise ratio greater than -5.9 dB and -9.11 dB
respectively. In addition, the above two systems have positive response
when the test images became uncompleted with percent until 14% and
40% respectively. The neural network systems are tested on 1508 aircraft
images some of them are noisy and uncompleted. The noise associated with aircraft images has been measured and
found to be uncorrelated. All the presented aircraft recognition methods
are invariant to translation and rotation except the first one is not
sensitive to scale variation.