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基于核密度估计的红外目标提取方法
引用本文:凌建国,刘尔琦,梁海燕,杨杰.基于核密度估计的红外目标提取方法[J].红外与毫米波学报,2006,25(6):434-438.
作者姓名:凌建国  刘尔琦  梁海燕  杨杰
作者单位:1. 上海交通大学,图像处理与模式识别研究所,上海,200240;中国航天科工集团公司二院,北京,100854
2. 中国航天科工集团公司二院,北京,100854
3. 上海交通大学,图像处理与模式识别研究所,上海,200240
基金项目:国家重点基础研究发展计划(973计划);航空基础科学基金
摘    要:提出了有效集成灰度、空间关系和局部标准差信息的新的核密度估计方案,据此设计了一种基于核密度估计的红外目标提取方法,即首先将图像分块,根据块的统计特征获得包含整个目标的约束区域;然后对目标约束区域和相应的背景采样区域进行核密度估计,这里背景采样区域指的是围绕着目标但又不包含目标的图像区域,由目标约束区域向外扩展而形成;最终通过对两种核密度估计对比的阈值判断即能获得所提取的目标.实验验证了所提出的算法简单有效.

关 键 词:目标提取  统计特征  核密度估计  局部标准差
文章编号:1001-9014(2006)06-0434-05
收稿时间:2005-11-08
修稿时间:2006-05-15

INFRARED TARGET EXTRACTION METHOD BASED ON KERNEL DENSITY ESTIMATION
LING Jian-Guo,LIU Er-Qi,LIANG Hai-Yan,YANG Jie.INFRARED TARGET EXTRACTION METHOD BASED ON KERNEL DENSITY ESTIMATION[J].Journal of Infrared and Millimeter Waves,2006,25(6):434-438.
Authors:LING Jian-Guo  LIU Er-Qi  LIANG Hai-Yan  YANG Jie
Affiliation:1. Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China; 2. Institute of the Second Academy, China Aerospace Science and Industry Corporation, Beijing 100854,China
Abstract:A new kernel density estimation scheme which incorporates gray values,spatial relation and local standard deviations information effectively was proposed.With this scheme,an infrared target extraction method based on kernel density estimation was designed.Firstly,the entire image was divided into blocks and the confined region that contained the entire target was obtained based on the statistical feature of each blocks.Secondly,the kernel density estimations of the confined region and the corresponding background sample region were estimated.Here,the background sample region is a surrounding image region of the target,but it doesn't contain the target.It is enlarged from the confined target region.Finally,the target was extracted with the contrast of two kernel density estimauions under a given threshold.The experimental results show that the proposed method is simple and efficient.
Keywords:target extraction  statistical feature  kernel density estimation  local standard deviation
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