| Summary: | Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. Computer Science Bibliography: leaves 109-120 This thesis presents an artificial neural network system for edge detection and edge enhancement. The system can accomplish the following tasks: (a) obtain edges; (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined "n the Hopfield neural network. A criterion is provided to find the final stable result which contains the enhanced edge measurement. -- The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators, the proposed fuzzy neural network obtains very good results.
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