摘要
使用Mexican hat函数作为特征检测算子,对图像进行局部区域提取和特征点提取,在此基础上,提出了一种结合局部区域和特征点的图像配准算法。使用Mexican hat检测算子和零交汇点检测的方法获得局部区域特征并进行初步匹配,然后使用基于不同尺度空间的Mexican hat检测算子进行特征点提取。将特征点按照局部区域进行分组,再对每一组内的特征点进行匹配操作。最后使用基于分组的随机采样一致性检验进行变换矩阵求解。算法使用Mexican hat特征检测算子进行两种图像特征的提取,对两种特征分别进行匹配,完成图像配准操作。实验结果表明,给出的Mexican hat特征检测算子在配准精度上不亚于当前主流检测方法,配准算法具有复现率高和运算速度快的优点。
An operator based on Mexican hat function was used for image local area and feature point detection. Then an image registration algorithm using the two kinds of features was proposed. The Mexican hat operator combining zero- crossing is used for local areas detection, and the feature points are detected by the operator on the different scale space. The image is partitioned into several regions by the local areas and the regions are matched. Then the points are grouped by the regions and matched in each group respectively. At last the image transaction function is gotten by the grouped random sample consensus. The algorithm in this work is based on two kinds of image feature detection and matching u- sing the Mexican hat function, and the experimental results show that the proposed algorithm has high alignment accu- racy and small computational volume.
出处
《计算机科学》
CSCD
北大核心
2014年第2期280-284,共5页
Computer Science
基金
国家自然科学基金:高维小样本自适应阵列信号处理研究及应用(61271293/F010305)资助
