Author
Abstract
The most popular applications of Hopfield neural network algorithm (HNN) are
pattern recognition and classification. But the HNN has some limitation like the local
minima (oscillation) problem. In this paper a novel method of combining an active
contour (snake) and an artificial neural network to behave together as pattern recognition
and classification is presented. The approach used the technique of the gradient vector
flow (GVF) that locate the boundary of target pattern (image) then pass it to a classifier
built by Hopfield algorithm to classify it according to one of the storage pattern. The
snakes can find the boundaries of objects so it is very accurate to take the shape of the
object wanted, that will eliminate the noise from the original image and reduce the bit
error rate of the Hopfield network to 0.215 and overcome the oscillation state in
recognition of the entered pattern. MATLAB 7 program have been used for the
simulation of the active contour and the pattern classification.
Keywords
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