提出了一种对来自多传感器的序列图像进行时间．空间配准的计算框架。该框架适用于摄像机静止的场合，而且所拍摄的图像序列中有运动目标存在，但在图像序列的开始阶段为静止背景。首先对静止背景进行配准，得到空间变换的初始估计；然后，利用运动目标质心间的对应关系得到时间变换的初始估计；最后，结合共同信息计算出最终结果。本框架的空间配准精度可得到亚像素级，时间配准精度可达到亚帧级。本框架已成功应用于可见光／红外图像序列的配准实验。

A computational framework for the temporal-spatial alignment of multi-sensor image sequences is presented in this paper. The framework is suitable to the circumstance where the cameras are static; the captured sequences contain moving objects but the initial segments of the sequences are frames of the static background. The framework first registers the static backgrounds of the sequences to yield the initial spatial transformation. Then it uses the correspondence of the centroids of moving objects to estimate the initial temporal transformation. Finally, mutual information is incorporated into this framework to compute the final temporal-spatial transformations. This framework, which can obtain a sub-pixel and sub-frame registration accuracy, has been successfully applied to a visible/infrared sequence alignment experiment.