目的 针对当前较多图像匹配算法主要通过对特征点的相似性进行度量来优化匹配特征点，忽略了特征点之间的投影相关性，导致算法的鲁棒性下降、匹配错误度较高的问题，文中提出了基于改进FAST算子耦合投影约束法则的图像匹配算法。方法 首先，利用FAST算子提取图像特征点，并通过Harris算子去除FAST算子中的伪特征点，充分获取稳定特征点。然后，利用圆域内像素点的高斯曲率值，对特征点进行描述。最后，利用归一化互相关系数（Normailized correlation coefficient，NCC）对特征点进行匹配。并通过特征点之间的投影关系函数计算特征点的投影值，并根据投影值建立投影约束法则，以去除错误配点，优化匹配精度。结果 实验数据显示，与当前图像匹配技术相比，所提算法具有更好地鲁棒性与匹配精度，在多种几何攻击下，所提算法的正确匹配率仍可维持在90%以上。结论 所提算法在各类几何变换下仍具有良好的匹配精度，在图像处理、信息安全等领域具有良好的参考价值。

The work aims to propose an image matching algorithm based on improved FAST operator coupled projection constraint rule for the current problems that the projection correlation between feature points is ignored which results in reduction of algorithm robustness and more matching errors since the current image matching algorithms mainly optimizes the matching feature points by measuring the similarity of feature points. Firstly, FAST operator was used to extract feature points of image, and Harris operator was used to remove pseudo feature points from feature points to obtain stable feature points. Then, the Gaussian curvature value of pixels in the circle domain was adopted to describe the feature points. Finally, the normalized cross correlation coefficient was used to match the feature points. The projection value of the feature points was calculated by the projection relation function between the feature points, and the projection constraint rule was established according to the projection value to remove the mismatch points and obtain the matching results. From the experiment data, the proposed algorithm had better robustness and matching effect than the current image matching algorithm. Under multiple geometric attacks, the correct matching rate of proposed algorithm was kept above 90%. The proposed algorithm still has good matching accuracy under various geometric algorithms and has good reference value in image processing, trademark retrieval and other fields.