基于改进SURF特征点的图像匹配方法

Image Matching Method Based on Improved SURF Feature Points

针对SURF算法在图像匹配过程中存在运算时间长、匹配正确率低等问题,采用DAISY描述符与RANSAC算法优化加速鲁棒特征(SURF),提出一种基于改进SURF特征点的图像匹配方法。首先,利用DAISY描述符结构简单、复杂程度低、匹配精度较高等特点,在SURF算法特征点检测的基础上采用DAISY特征描述符替换SURF特征描述算子,并采用RANSAC算法删除误匹配点。实验表明,在图像模糊、角度旋转、光照变化、JPEG压缩比变化等多种复杂情况下,该算法相较于SURF算法具有更好的鲁棒性,能提高匹配对数,剔除误匹配点,减少算法运行时间,匹配正确率均高达95%以上。

Aiming at the problems of SURF algorithm in image matching,such as long operation time and low matching accuracy,DAISY descriptor and RANSAC algorithm are used to optimize accelerated robust feature(SURF),and an image matching method based on improved SURF feature points is proposed.Firstly,taking advantage of DAISY descriptor′s simple structure,low complexity and high matching accuracy,DAISY descriptor is used to replace SURF feature description operator based on SURF algorithm feature point detection,and RANSAC algorithm is used to delete mismatched points.The experiment shows that the algorithm is more robust than SURF algorithm in many complex situations,such as image blur,angle rotation,illumination change,JPEG compression ratio change,etc.It can improve the matching logarithm,eliminate the wrong matching points,reduce the running time of the algorithm,and the matching accuracy is more than 95%.