PCNN automatic parameters determination in image segmentation based on the analysis of neuron firing time

Abstract

Pulse Coupled Neural Network (PCNN) model has been widely used in digital image processing, but its parameters determination is always under a difficult situation. This paper analyzed the firing time of the coupled linking PCNN, and indicates the difference between theoretical firing time and actual firing time when the neuron is under the influence of the neighboring neurons. By analyzing the influence of the parameters on the coupling effects of neighboring neurons, a new method of setting parameters is proposed in image segmentation. Revealing that only with the proper parameters setting, can the theoretical firing time and the actual firing time of the neuron be consistent, so that the pulse burst characteristics of PCNN can be truly realized. For Lena image segmentation, etc, the similar effects is obtained with the traditional experience parameters, and showing validity and efficiency of our proposed method. © 2011 Springer-Verlag Berlin Heidelberg.