摘要
针对不规则图像成分复杂、特征形状无法采用经典模型拟合等问题,提出了基于改进型脉冲耦合神经网络(PCNN)的自适应分割算法。该算法在原有PCNN模型基础上,对神经元反馈输入函数和动态阈值函数进行了修正,同时对神经元的输出采用多级输出模型,从而实现对不规则图像的分割。仿真实验表明,改进后的算法能够实现不规则图像的自适应分割,鲁棒性较好。
Concerning the characteristics of complex components of irregular images and random alignment of irregular spot without proper fitting mathematics model, an adaptive segmentation algorithm with improved Pulse Coupled Neural Network （PCNN） was proposed in the paper. On the basis of basic PCNN model, the neurons feedback input function and dynamic threshold function were modified and multi-level output model for the neuron output was designed to implement the segmentation process as well. Simulation experiment shows that the improved PCNN has better robustness and can realize the adaptive segmentation of irregular image.
出处
《计算机应用》
CSCD
北大核心
2008年第3期650-652,共3页
journal of Computer Applications
基金
天津市自然科学基金重点资助项目(07JCZDJC05800)
北京市科技计划项目(Z0005190040831)
关键词
不规则图像
脉冲耦合神经网络
动态阈值
自适应分割
irregular image
Pulse Coupled Neural Network （PCNN）
dynamic threshold
adaptive segmentation
