摘要
复杂背景下,特别是在环境与人体温度相差不大的情况下,红外运动人体目标与背景的灰度值会非常相似,准确的红外人体分割是一个难题。对基于混合高斯模型的背景减除法进行改进,在二值化阶段采用改进型的脉冲耦合神经网络(PCNN)进行精细分割,利用多模态免疫进化算法(MIEA)自动确定PCNN分割参数。仿真实验结果表明,该算法图像分割精度高,实现了快速自动分割,取得了较为理想的图像分割效果。
The accurate segmentation of infrared body is a difficult problem under complicated background,especially in the environment that the gray values are very similar between the infrared human movement target and background when their temperatures vary slightly.Therefore,the background subtraction method based on Gaussian mixture model is improved.The fine segmentation is implemented by the modified Pulse Coupled Neural Network（PCNN） in its binary stage,and meanwhile the PCNN segmentation parameters are determined by using the multi-modal immune evolution algorithm（MIEA）.The simula- tion results show that this algorithm is achieved fast automatic segmentation,and has gotten the ideal effect of image seg- mentation that its precision is high.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第14期13-16,共4页
Computer Engineering and Applications
基金
国家自然科学基金 No.60673132~~
关键词
混合高斯模型
脉冲耦合神经网络
多模态免疫进化算法
图像分割
Gaussian Mixture Model （GMM）
Pulse Coupled Neural Network （PCNN）
Multi-modal Immune Evolution Algorithm（MIEA）
image segmentation
