结合快速鲁棒性特征改进ORB的特征点匹配算法  

Improved feature points matching algorithm based on speed-up robust feature and oriented fast and rotated brief

作　　者：白雪冰[1,2] 车进[1,2] 牟晓凯[1,2] 张英[1,2] 

机构地区：[1]宁夏大学物理电气信息学院,银川750021 [2]宁夏沙漠信息智能感知重点实验室(宁夏大学),银川750021

出　　处：《计算机应用》2016年第7期1923-1926,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61162020)~~

摘　　要：针对定向二进制简单描述符(ORB)算法不具备尺度不变性的问题,提出一种结合快速鲁棒性特征(SURF)算法和ORB的改进算法。首先,利用Hessian矩阵检测特征点的方法,使得提取出的特征点具有尺度不变性;然后,用ORB生成特征描述子;接着采用K-近邻算法进行粗匹配;最后,通过比率测试、对称测试、最小平方中值(LMed S)定理进行提纯。尺度变化时,该算法比ORB的匹配精度提高了74.3个百分点,比SURF的匹配精度提高了4.8个百分点;旋转变化时,该算法比ORB的匹配精度提高了6.6个百分点;匹配时间高于SURF低于ORB。实验结果表明,改进算法不仅保持了ORB的旋转不变性,而且具备了尺度不变性,在不失速度的前提下,匹配精度得到较大提高。Focusing on the issue that the Oriented fast and Rotated Brief（ ORB） algorithm does not have scale invariance, an improved algorithm based on Speed-Up Robust Feature（ SURF） and ORB was proposed. First, the feature points were detected by Hessian matrix, which made the extracted feature points have scale invariance. Second, the feature descriptors were generated by the ORB. Then the K-nearest neighbor algorithm was used for rough matching. Finally, the ratio test, symmetry test, the Least Median Squares（ LMed S） theorem was used for purification. When the scale changed, the proposed algorithm＇s matching precision was improved by 74. 3 percentage points than the ORB and matching precision was improved by 4. 8 percentage points than the SURF. When the rotation changed, the proposed algorithm＇s matching precision was improved by 6. 6 percentage points than the ORB. The proposed algorithm＇s matching time was above the SURF, below the ORB. The experimental results show that the improved algorithm not only keeps the rotation invariance of ORB, but also has the scale invariance, and the matching accuracy is improved greatly without decreasing the speed.

关 键 词：特征点匹配 尺度不变性 旋转不变性 比率测试 对称测试 最小平方中值定理 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]