基于脉冲耦合神经网络的图像分割

doi:10.11896/j.issn.1002-137X.2017.04.064

Abstract

Abstract: Traditional pulse coupled neural network (PCNN) model needs to set a lot of parameters in processing image segmentation and can not segment the images with low contrast precisely.In order to solve the problems,an improved image segmentation algorithm was proposed based on a simplified PCNN model.In the simplified mode,the number of parameters required in the traditional PCNN model was reduced.In the improved algorithm,the model parameters were set adaptively according to the image pixel space and gray features,and the image grayscale expected mean was obtained as the image segmentation threshold according to the image gray histogram.Therefore,the improved algorithm has no iteration stop condition need to choose,just once the ignition process the method can complete the image segmentation effectively.The experimental results show that this method is accurate in image segmentation,especially in image texture details,and the final result is better than some methods,such as manual adjustment method of PCNN parameters and Otsu method.

Key words: Pulse coupled neural network,Image segmentation,Parameter setting,Grayscale expected mean

WANG Ai-wen and SONG Yu-jie. Image Segmentation Based on Pulse Coupled Neural Network[J].Computer Science, 2017, 44(4): 317-322.

References

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