Study on Self-adaptive Pulse Coupled Neural Network and Its Application in Fields of Image Processing

The standard Pulse Coupled Neural Networks (PCNN) has been widely used in the image processing, however, it is hard to set plenty parameters of PCNN efficiently which limited its capability for image processing. Based on the learning rules, PCNN was optimized through running its parameters adaptively. A gradient descent algorithm was adopted to search parameters which could reduce the error between the desired output and the actual output gradually according to the least mean square principle. The traditional PCNN model is used to image feature extraction, its output features are rotation, scale and shift invariant, but it is sensitive to illumination, therefore the adaptive parameters PCNN is used for image feature extraction when the stimuli's illumination (intensity or contrast) is varied. The results are shown that the application efficiency of feature extraction is improved.