基于二维Gabor小波变换的角点匹配算法

A Corner Match Algorithm Based on the Two-Dimensional Gabor Transform

图像配准研究的核心问题在于提高配准的速度和精度,而图像配准的结果主要取决于特征的匹配精度。为了提高特征匹配精度,本文提出了一种基于二维Gabor小波变换的角点匹配算法。该算法首先采用改进的Harris角点检测方法提取角点,得到角点位置的坐标,利用多个二维Gabor小波模板对参考图像和待配准图像进行滤波,从滤波图像中提取角点坐标处的复Gabor小波系数,并以此作为角点的特征描述,然后引入两种相似性度量因子对角点进行匹配。通过对不同图像进行大量的实验,该算法在选择合适的参数,同时采用最长公共子序列度量因子的情况下,能成功提取较多的同名点对,并且能够取得较高的匹配率。

The core issue of image registration is to increase the speed and accuracy of registration,but the result of the registration much depends on the matching precision of features.In order to increase the matching precision of features,an algorithm based on the two-dimensional Gabor wavelet transform for corners matching is proposed in this paper.Firstly,it adopts an improved Harris corner detector to extract corners and obtains the coordinates of corner positions,and utilizes multi-masks of two-dimensional Gabor wavelet to filter the reference image and unregistered image to extract the duplicate Gabor wavelet coefficient of corner coordinates from filtering the image,and takes this as the feature description of corners.Secondly it introducs two kinds of similarity measurement to match corners.Adopting the longest common subsequence is successful to extract more points of the same name and has a higher match rate,which has been proved by multi-experiments when parameters are chosen appropriately.