PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection

The Pulse coupled neural network (PCNN) has been widely used in digital image processing, but the automatic parameters determination is still a difficult aspect, which becomes the focus of PCNN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neurons, and reveal the “mathematics firing” phenomenon of PCNN. Based on this, we propose a new method of automatic parameters determination based on both eliminating the “mathematics firing” and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Experimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge detection of the test images.