目的 为了解决提高图像匹配算法的匹配精度与鲁棒性。方法 设计基于区域自适应模型耦合向量约束规则的图像匹配算法。首先引入采用上下文信息的显著性分析方法，提取图像的显著区域和非显著区域。根据区域的显著性特征构造区域自适应模型，用以动态调整FAST算法中的灰度阈值，提取图像中的特征点。然后，通过欧氏度量将特征点邻域内的点分为长、短点集；通过长点集生成特征方向，利用短点集生成特征向量，以获取特征点的描述符。最后，对特征点之间的Hamming距离进行度量，实现特征点的匹配。利用匹配特征点组成的向量建立向量约束规则，对匹配特征点进行优化，完成图像匹配。结果 实验结果表明，与当前图像匹配技术相比，所提算法具有更高的鲁棒性与匹配正确度，当目标旋转角度达到100°时，其匹配准确率仍可达到88.95%。结论 所提算法具有良好的适应性，在遇到几何变换时，具有较好的匹配精度，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 improve the matching accuracy and robustness of image matching algorithm. The image matching algorithm based on region adaptive model coupled vector constraint rule was designed. First, the saliency analysis method using context information was introduced to extract the salient regions and non-significant regions of the image. According to the saliency of the region, a region adaptive model was constructed to dynamically adjust the gray threshold in the FAST algorithm and extract the feature points in the image. Then, the points in the neighborhood of the feature point were divided into long and short point sets by the Euclidean metric. The feature direction was generated by the long point set, and the feature vectors were generated by the short point set to obtain the descriptors of the feature points. Finally, the Hamming distance between feature points was measured to achieve the matching of feature points. The vector constraint rules were established by means of the vectors composed of matching feature points to optimize the matching feature points, so as to complete the image matching. The experimental results showed that, compared with the current image matching technology, the proposed algorithm had higher robustness and matching accuracy. When the target rotation angle reached 100°, its matching accuracy could still reach 88.95%. With good adaptation and better matching accuracy in the case of geometric transformation, the proposed algorithm has good reference value in the fields of image processing, information security and so on.