基于灰度相关性的改进PCNN图像自动分割算法
Automatic Image Segmentation with PCNN Algorithm Based on Grayscale Correlation
-
摘要: 为了利用脉冲耦合神经网络﹙pulse coupled neural network,PCNN)实现精确的图像自动分割,对PCNN模型进行改进,提出首先根据图像局部灰度相关性和欧氏距离建立连接权矩阵,然后利用最小方差比准则自动判定PCNN的循环次数,实现图像的自动分割,仿真实验结果表明,该方法可实现PCNN算法迭代次数的自动判定,算法适用性强,并可得到较好的分割效果.Abstract: In order to utilize pulse coupled neural networks (PCNN) for precise automatic image segmentation,in this paper,we improved PCNN model.Firstly,we established a connection weight matrix based on the image local gray correlation and Euclid distance,then,used minimum variance ratio criterion determines cycle times of PCNN automatically,achieved automatic image segmentation.The simulation results showed that this method could determined PCNN number of iterations automatically,and has a strong feasibility and better segmentation results.
下载:
