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）

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）