摘要
针对灰度差异较大的红外图像和可见光图像,提出了一种基于互相关系数和Canny边缘区域相结合的配准算法,以改进的互相关系数作为相似性度量函数,只提取图像的边缘及其附近的区域,剔除其他互相关性低的部分,采用粒子群优化算法搜索使得度量函数达到最值时的空间变换参数的值.实验结果表明,克服了红外和可见光图像相关性较差的缺点,能够避免由灰度差异带来的多源图像配准不精确的情况,可以实现红外和可见光的配准,并且能够保证误差在较小的范围之内.
A new algorithm aimed at infrared and visible image registration based on crosscorrelation coefficient and Canny edge region was presented：regarded the improved Crosscorrelation coefficient as the similarity function,extracted the edge region and its nearby area,removed other regions with low correlation,used the particle swarm optimization（PSO）to seek.It shows that this algorithm can overcome the shortcomings which infrared and visible images are poor correlated and avoid the bad matching between multi-sensor images.Multi-sensor image registration could be achieved and small error is guaranteed.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2016年第3期320-325,共6页
Transactions of Beijing Institute of Technology
关键词
互相关系数
边缘区域
边缘提取
度量函数
图像配准
cross-correlation coefficient
edge region
edge extraction
similarity function
image registration
