目的 特征点匹配算法是当今计算机图像处理领域的研究热点，但是大多数现存的方法不能同时获得数量多和质量优的匹配。鉴于此，基于SURF （speeded-up robust features）算法，通过引入极线约束来提高特征匹配效果。方法 首先使用SURF算法检测和描述图像特征点，然后使用RANSAC （random sampling consensus）方法计算匹配图像之间的基础矩阵，通过该基础矩阵计算所有特征点的极线。再引入极线约束过滤掉错误匹配，最终获得数量与质量显著提高的匹配集合。结果 实验结果表明，该方法获得的匹配具有高准确度，匹配数目与原约束条件相比可高达2～8倍。结论 本文方法实现过程简单，不仅匹配准确度高且能够大大提高正确的特征匹配数，适用于处理不同类型的图像数据。

Objective Feature matching is one of the most important research topics in the field of image processing. However, most available methods fail to achieve satisfying quantitative and qualitative matches simultaneously. In this study, we introduced epipolar constraint into speeded-up robust features (SURF) feature matching, thereby achieving significant improvement. Method In this method, the SURF algorithm was adopted to detect the feature points of each studied image. Then, the fundamental matrix was calculated using random sample consensus (RANSAC) and was used to obtain the epipolars of all the points. Finally, a constraint was introduced into the epipolars to filter error matches. Consequently, significantly improved matches with enhanced quantity and quality were achieved. Result The experimental results indicate that compared with the old method, our method cannot only obtain matches with high accuracy but can further achieve an increase of twofold to eightfold in quantity. Conclusion The process and implementation of the proposed method are simple and accurate. Moreover, the method can increase the number of correct matches and handle different types of images.