中文摘要：

通过对传统脉冲耦合神经网络（PCNN）模型的改进，在模型的输入端加入目标区域的边缘数据，使最高灰度级不同的非连通神经元同期点火，实现了多目标区域同时分割。给出了影响同期点火激励范围的主要参数口的自动设定方法，并设计了基于图像最大熵准则的自动分割算法。用分割精度评价准则验证了所提出方法的有效性。实验证明，对于低噪声污染的图像，改进的PCNN模型在多目标识别中的正确接受率达到95％以上，明显优于经典的Fastlinking模型。

英文摘要：

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 Fastlink. ing. model.