摘要
根据模板和全局最优子图的特点及其相互关系给出2个判据,对归一化互相关算法进行了改进.首先计算模板自相关值,再利用快速傅里叶变换方法计算互相关矩阵,利用第1个判据大幅缩小可能解的范围,减少匹配时间,然后利用第2个判据生成一个规模更小的候选最优解集合,最后确定全局最优解.实验结果说明,改进的归一化互相关算法能加快匹配速度,且能有效地提高图像匹配的准确率.
Some improvements were made for normalized cross correlation based on two criterions according to the characteristics of template and image and the interrelationship between them.The auto-correlation of the template was calculated at first,and the cross correlation between template and image was gained based on fast Fourier transform.Then the first criterion was used to shrink the range of the possible solutions,which could shorten the matching time;and the second criterion was applied to generating a more miniature set,in which the solution with the maximal normalized cross correlation was the global optimal solution.Experiments show that the normalized cross correlation based on the given criteria can speed the computing with an enhanced matching precision.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第8期1233-1237,共5页
Journal of Tongji University:Natural Science
基金
"十一五"国家科技支撑计划(2009BAG11B02)
关键词
归一化互相关算法
判据
匹配速度
匹配准确率
normalized cross correlation
criterion
matching speed
matching precision
