计算机与数字工程2018，Vol.46Issue(2)：375-381,406,8.DOI:10.3969/j.issn.1672-9722.2018.02.033
一种复杂背景环境下的改进型PCNN图像分割算法
An Improved PCNN Image Segmentation Algorithm in Complex Background Environment
刘军 1李子毅1
作者信息
- 1. 兰州理工大学机电工程学院 兰州730050
- 折叠
摘要
Abstract
For the traditional image segmentation algorithm in complex background environment,there are some problems such as low segmentation precision and poor anti-interference and so on.This paper presents an Improved Pulsed Coupled Neural Network(IPCNN)image segmentation algorithm.The algorithm takes into account the gray level distribution information of image pixels and the spatial position information between pixels.Based on the simplified PCNN model,the initial threshold is optimized by combining the two-dimensional OTSU method,and in order to improve the real-time performance of the algorithm,a fast recursive formula is derived and given.At the same time,it is different from the traditional PCNN which determins the key parameters of the model by experience or a large number of experiments.The IPCNN determines the connection strength coefficient by calculating the local gray mean square error of the image based on PCNN coupling characteristics,with consider of image space and gray level char-acteristics. The connection weight matrix is determined by considering the differences between spatial and gray values of pixel points.At last,the segmentation result is assessed based on the maximum information entropy principle and the adaptive automatic segmentation for the target object is realized.Numerical experiments show that the proposed algorithm has advantages over the tradi-tional PCNN algorithm in fast image segmentation,clear contour segmentation and strong anti-interference performance.关键词
机器视觉/图像分割/脉冲耦合神经网络/自动分割Key words
machine vision/image segmentation/improved pulse coupled neural network/automatic segmentation分类
信息技术与安全科学引用本文复制引用
刘军,李子毅..一种复杂背景环境下的改进型PCNN图像分割算法[J].计算机与数字工程,2018,46(2):375-381,406,8.
