针对传统图像匹配算法特征点检测稳定性和准确性差的问题,提出一种尺度不变性的基于中心环绕滤波器检测(SCFD)的图像特征点匹配算法.首先,构建多尺度空间,利用中心环绕滤波器检测图像在不同尺度下的特征点,采用Harris方法和亚像素插值获得稳定的特征点;其次,联合快速定向旋转二进制稳健基元独立特征(BRIEF)(ORB)算法确定特征点的主方向,构建特征点描述算子;最后,采用汉明距离完成匹配,通过最小平方中值(LMedS)定理和最大似然(ML)估计剔除误匹配点.实验结果表明,在尺度变化时,所提算法的匹配精度达到96.6％,是ORB算法的2倍;其运行时间是尺度不变特征变换(SIFT)的19.8％,加速鲁棒性特征(SURF)的28.3％.所提算法能够有效提高特征点检测的稳定性和准确性,在视角、尺度缩放、旋转、亮度等变化的情况下具有较好的匹配效果.%Aiming at the problems of poor stability and accuracy of feature point detection in traditional image matching algorithms,a new image feature point matching algorithm based on Scale-invariant Center surround Fiber Detection (SCFD)was proposed.Firstly,a multi-scale space was constructed,a center surround filter was used to detect feature points of a image at different scales,Harris method and sub-pixel interpolation were applied to acquire the stable feature points.Secondly,Oriented fast and Rotated Binary Robust Independent Elementary Feature (BRIEF) (ORB) algorithm was combined to confirm the main direction of feature points and construct the description operator of feature points.Finally,Hamming distance was used to complete the matching,Least Median Squares (LMeds) theorem and Maximum Likelihood (ML) estimation were used to eliminate wrong matching points.The experimental results show that,the matching precision of the proposed algorithm is up to 96.6％,which is 2 times of that of the ORB algorithm when the scale changes.The running time of the proposed algorithm is 19.8％ of that of Scale Invariant Feature Transform (SIFT) and 28.3％ of that of Speed-Up Robust Feature (SURF).The proposed algorithm can effectively improve the stability and accuracy of feature point detection,and has better matching effects under the circumstances of different angle of view,scale scaling,rotation change and brightness variation.
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