目前的图象序列特征点对应方法是建立在相邻图象间的特征点在运动形式上变化不大，即相邻两帧图象间的时间间隔较小这样的一个假设之上的，但当相邻图象间的时间间隔较大时，则这些方法很难找到对应的特征点.为此，提出了一个由粗到细解决图象序列特征点对应的新方法，该方法首先进行粗定位，即利用极指数栅格方法来得到运动后目标特征点的大致范围 ;然后通过细定位来得到对应的特征点.为了使人们对该方法有一全面了解，还介绍了该方法的原理，并给出了实验结果.实验证明，该方法可以很好地解决时间间隔较大的两帧图象间的特征点对应问题，其最大的优点是比通常的方法简单有效.

The current approaches to feature point correspondence are based on the hypothesis that the displacements for feature points between consecutive frames are small. That is, there is very short time interval between successive two frames in image sequences. These approaches are difficult to find the corresponding feature points when the time interval is large. In the paper, a new approach to feature point correspondence is proposed. It includes two steps. First, approximate location of feature points of moving targets can be obtained using polar-exponential grid sampling and log-polar coordinate mapping. The parameters of feature point position, including translation, rotation and scale, are got in log-polar coordinate and Cartesian coordinate. Then the corresponding feature points can be got with available traditional means, because feature points corresponding when the time interval is large turns into feature points corresponding when the time interval is small by the first step of the approach. The best advantage of the method is simple and efficient. In the paper, the principle of the method is introduced and the result of experiment is given. It turns out that the problem of feature point correspondence can be finally solved when the time interval between consecutive frames in image sequences is large, and that the first step of the approach is enough to resolve feature point correspondence of plane object moving.