基于改进型PCNN的不规则图像自适应分割算法研究  

Study on adaptive segmentation of irregular image based on improved PCNN

作　　者：冯登超[1] 杨兆选[1] 王哲[2] J．M．Dias Rereira 

机构地区：[1]天津大学信息学院,天津300072 [2]太原理工大学信息学院,太原030024 [3]塞图巴尔理工大学系统信息系,葡萄牙塞图巴尔2910-761

出　　处：《计算机应用》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)

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.

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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]