目的 针对可见—红外图像之间配准点的数量不足、分布严重不均匀以及配准点之间的错配率高这3个核心问题，提出一种基于自适应特征点检测的可见—红外图像配准方法。方法 本文提出的自适应特征点检测方法，以Harris corner作为基本特征点；以特征点数目与空间分布为检测目标，从而自动地估计合适不同空间位置的特征点的检测阈值。在特征点对匹配中，将梯度方向与互信息相融合有效地添加了相似性函数的空间位置信息。结果 自适应Harris corner检测方法能够有效地提供空间分布均匀、数量充足的特征点。而梯度方向与互信息相融合的相似性匹配函数提高特征点的匹配率20%，降低配准误差50%。结论 本文提出的多传感器图像配准方法能够快速、准确地实现可见光图像与红外图像之间的配准，在CCD-IR图像融合领域具有很好的实用价值。

Objective Multi-sensor image registration has three basic problems. (1)Detected feature points from multi-sensor images are insufficient. (2)The spatial distribution of detected feature points is unbalanced. (3)The matching result of the detected points cannot easily achieve high performance. A new CCD-IR image registration algorithm is proposed in this study to address the aforementioned problems, which includes an adaptive Harris corner detection approach and a new feature point matching measure function based on normalized mutual information and gradient orientation. Method In adaptive feature point detection, the quantity and spatial distribution of the feature point are as synchronous as the objective function. Different detection thresholds are then automatically assigned to the feature points according to their spatial position information. Moreover, normalized mutual information is combined with gradient orientation in the feature matching process to construct a new matching measure function. Result Experimental results show that the proposed adaptive feature detection approach can provide sufficient and uniform distributed feature points. The proposed mew matching measure function improved the point matching success rate by approximately 20%, whereas it decreased the registration error approximately 50%. Conclusion This study proposes a novel CCD-IR image registration algorithm, which is shown have low complexity, accuracy, and practicability. Thus, it can be applied to CCD-IR image fusion application.