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Neuro-Snake Pattern Recognition And Classification Using Gradiant Vector Flow (Gvf And Hnn) | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 27, Issue 5, 2009, Pages 973-982 PDF (424.91 K) | ||
| DOI: 10.30684/etj.27.5.11 | ||
| Author | ||
| Wissam Hassan Ali | ||
| 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 | ||
| GVF; Neural; HNN; Pattern Recognition; image process | ||
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