Abstract: Feature point matching is an important subject in computer vision field. For the lack of scale invariance characteristics of ORB（ORiented Brief）algorithm, this paper proposes an improved method based on the combination of SURF（Speeded-Up?Robust?Features）and ORB, whose name is SUORB（Speeded-Up?ORiented Brief）. Firstly, the scale spaces are built, in which the stable extreme points are detected in order to get the feature points with scale invariance information. Then, the feature points are described by the ORB descriptors with rotation invariance. Finally, Hamming distance is used to finish the final matching task. The experimental results show that SUORB has solved the deficiencies that ORB has little scale invariance. SUORB improves the matching accuracy, compared to ORB when images have scale changes. At the same time, the matching speeds of SUORB and ORB are almost the same.

Key words: feature points matching, multi scale space, combination of algorithms, ORiented Brief（ORB）, Speeded-Up?Robust?Features（SURF）

摘要： 在计算机视觉领域中特征点匹配是一个重要课题。针对ORB（ORiented Brief，方向描述符）算法缺少尺度不变性的特点，将SURF（Speeded-Up?Robust?Features，快速鲁棒特征）算法与ORB相结合，提出了基于算法组合的改进算法SUORB（Speeded-Up?ORiented Brief，快速方向描述符）。组合算法的基本思路是利用SURF算法建立多尺度空间，然后通过ORB算法为检测出的特征点建立描述符，最后根据生成的二进制描述符实现特征点匹配。实验结果表明，SUORB算法基本弥补了ORB算法的不足，若图像尺度发生变化，SUORB匹配算法比ORB匹配算法的准确度明显提高；同时SUORB算法保留了ORB算法的快速性。

关键词: 特征点匹配, 多尺度空间, 组合算法, 方向描述符（ORB）, 快速鲁棒特征（SURF）