摘要
针对红外图像对比度差、边缘模糊的特点,提出了一种基于时空联合的红外序列图像目标提取的新方法。算法充分利用了红外目标的亮度特征、背景信息以及运动信息。时域分割中通过建立帧差图像背景的高斯分布模型,采用变化检测模板来确定红外目标约束区域。然后,构造图像像素与区域之间的空间关系隶属度矩阵并约束到传统的模糊聚类算法中,空域分割则利用该模糊聚类来对目标约束区域进行有效分割。最后将时空分割结果融合便能实现最终的红外目标提取。实验结果表明,该方法简单有效,能准确提取动态场景中的红外目标。
A novel extraction algorithm for infrared moving target is proposed based on spatio-temporal information. The proposed algorithm efficiently utilizes the target intensity feature, surrounding background and the moving information. In time domain segmentation, Gauss distribution model of frame difference background is established to determine the infrared target region via change detection mask. The spatial relation matrix between pixel and region is constructed to constrain the classical fuzzy C-Means clustering （FCM）, And then, the region which contains the entire target is segmented efficiently based on the improved fuzzy clustering algorithm. At last, infrared target extraction is achieved by the fusion of spatio-temporal results. Experimental results verify the effectiveness and robustness of this extraction algorithm which can extract the infrared target correctly.
出处
《光电工程》
CAS
CSCD
北大核心
2008年第5期50-54,139,共6页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(60572080,60772151)
关键词
时空联合
红外图像序列
模糊聚类
目标提取
spatio-temporal information
infrared image sequence
fuzzy clustering
target extraction
