Image segmentation method based on an improved PCNN

The methods proposed in this paper improved the classical pulse coupled neural network (PCNN). Through adding the extracted edges of the objects into the classical PCNN, the synchronous bursts of non-linking neurons with different input were generated in the proposed PCNN model in order to realize the multi-object segmentation. The paper provided the criterion of choosing the dominant parameter (the linking strength β) automatically, which determines the synchronous-burst stimulus range. At the same time, the paper designed an automatic image segmentation algorithm in order to stimulate its application in the testing segmentation precision. The experimental results for the low-noise image show that the correct rate of the proposed model is over 95% and the property is superior to the classical Fast linking model.