摘要
当前红外和可见光图像配准未考虑灰度、空间的位置情况,匹配结果的平均归一化修正检索秩(A_(r))较高。因此,提出融合特征的红外与可见光异源图像匹配算法。分析待匹配图像的中矩形区域灰度差值特征,绘制Haar特征密度分布图,并基于此实现图像滤波。通过离散变换(K-L)提取滤波后图像灰度特征、像素密度特征和纹理特征,完成多图像特征的压缩与融合。基于融合特征,计算待匹配红外图像与可见光图像的格贴近度,得到异源图像匹配结果。实验结果表明:所提方法应用后,异源图像匹配结果的A_(r)平均值仅为0.35,更好地满足了图像匹配要求。
current infrared and visible image registration does not consider the gray level and spatial position,and the average normalized correction retrieval rank of the matching results is high.Therefore,a fusion feature based infrared and visible light heterogenous image matching algorithm is proposed.Analyze the gray difference characteristics of the middle rectangular area of the image to be matched,draw the Haar feature density distribution map,and implement image filtering based on this.Extracting filtered image grayscale features,pixel density features,and texture features through discrete transformation(K-L),completing the compression and fusion of multiple image features.Based on fusion features,calculate the lattice closeness between the infrared image to be matched and the visible light image,and obtain the matching results of heterogeneous images.The experimental results show that after the application of the proposed method,the average A_(r) value of the heterogenous image matching results is only 0.35,which better meets the image matching requirements.
作者
王智军
王鹏
WANG Zhijun;WANG Peng(School of Mathematics and Computer Science,Chifeng University,Chifeng Inner Mongolia 024000,China)
出处
《激光杂志》
CAS
北大核心
2024年第5期127-132,共6页
Laser Journal
基金
内蒙古教育厅课题(No.NJZY23030)。
关键词
红外图像
可见光图像
纹理特征
特征融合
格贴近度
图像匹配
infrared image
visible light images
texture features
feature fusion
grid proximity
image matching
