Automatic image segmentation based on PCNN with adaptive threshold time constant

PCNN is a novel neural network model to simulate the synchronous phenomenon in the visual cortex system of the mammals. It has been widely used in the field of image processing and pattern recognition. However, there are still some limitations when it is applied to solve image processing problems, such as trial-and-error parameter settings and manually selection of the final results. This paper studies a simple model of PCNN(S-PCNN) and applies it to image segmentation. The main contributions of this paper are: (1) A new method based on the simplified model of PCNN is proposed to segment the images automatically. (2) The parameter settings are studied to ensure that the threshold decay of S-PCNN would be adaptively adjusted according to the overall characteristics of the image. (3) Based on the time series in S-PCNN, a simple selection criteria for the final results is presented to promote efficiency of the proposed method. (4) Simulations are carried out to illustrate the performance of the proposed method.