摘要
在图像处理中,精确的图像分割可以加快后续的处理工作,具有更好的应用性。根据近些年提出的脉冲耦合神经网络模型在图像分割中的应用,给出其在图像分割中的基于熵函数、准则函数、参数调整和改进的脉冲耦合神经网络模型的4种方法,并对各个方法进行了综述。最后根据模型的基本特性和文献进展情况,给出脉冲耦合神经网络模型在图像分割中未来的研究方向。
In the image processing,accurate image segmentation can speed up the follow-up processing,and has better ap-plicability. The four methods based on entropy function,criterion function,parameter adjustment and improved pulse coupled neural network （PCNN） model used in the image segmentation are offered according to the application of PCNN model de-veloped in recent years. Each method is summarized. Finally,the future research direction of PCNN model in image segmenta-tion is pointed out according to the basic characteristics and the literature progress of model.
出处
《现代电子技术》
2014年第2期38-41,共4页
Modern Electronics Technique
基金
青年科学基金(51309068)
关键词
脉冲耦合神经网络
图像分割
熵
参数调整
pulse coupled neural network
image segmentation
entropy
parameter adjustment
