图像特征点匹配算法的研究

Research on Feature Points Matching Algorithm of Sequence Image

特征点匹配是计算机视觉领域的一个重要课题,针对ORB算法在特征点匹配基本上没有尺度不变性,与SURF理论算法相结合,提出基于算法组合的改进算法SUORB。首先生成多尺度空间,并在多尺度空间里检测稳定的极值点,以便提取出的特征点具有尺度不变信息;然后使用ORB描述符来描述特征点,生成旋转不变性的二进制描述符;最后实现特征点匹配。实验结果表明,SUORB有效地解决ORB的缺陷,在图像尺度变化时,SUORB匹配算法比ORB匹配算法的准确度明显提高;同时SUORB和ORB两种算法的匹配速度很接近。

Feature point matching is an important subject in computer vision field, for the lack of scale invariance characteristics of ORB algorithm, proposes an improved method based on the combination of SURF and ORB, which name is SUORB. Firstly,builds the scale spaces,in which the stable extreme points are detected in order to get the feature points with scale invariance information. Then, describes the feature points by the ORB descriptors with rotation invariance. Finally, Hamming distance is used to finish the final matching task. The experi- mental 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.