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A new approach for edge detection of noisy image based on CNN
Author(s) -
Zhao Jianye,
Wang Haiming,
Yu Daoheng
Publication year - 2003
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.210
Subject(s) - computer science , image (mathematics) , enhanced data rates for gsm evolution , edge detection , pixel , image processing , artificial intelligence , function (biology) , cellular neural network , algorithm , artificial neural network , pattern recognition (psychology) , evolutionary biology , biology
A new approach for edge detection of noisy image by cellular neural network (CNN) is proposed in this paper. In order to get the reasonable template, the statistical characteristics of image are utilized, and Gibbs image model is employed to describe the stochastic dependence of an edge pixel on its neighbourhood. Based on stochastic edge image models, edge detection of noisy image is equivalent to seeking a minimum of a cost function. If the template of CNN is designed carefully, the energy function can be mapped properly to the cost function of stochastic edge image model, then CNN can be used for seeking the minimum of cost function. Genetic algorithm is efficient in the field of optimization, and we also utilized this algorithm to get the correct form of template. The results of computer simulation confirm that the new approach is very effective. Furthermore, this result also confirms that we can design template for many different questions based on statistical image model, and the area of application of CNN will be widened. Copyright © 2003 John Wiley & Sons, Ltd.