中文摘要：

提出了一种基于特征点匹配的视频序列多参量运动估计算法，即将初始特征点域，进行梯度方向上的极坐标变换，并依据正交特征，作方向轴上的一维投影，构建出新的特征曲线，从而将孤立的特征点匹配转化成特征曲线相关。依据曲线的相关性，完成特征点对的匹配，最后使用最小二乘解算平移、旋转、缩放等变换参量。实验结果表明：算法的特征点误匹配率〈5%缩放误差〈0．1%，平移误差〈0．1像素；各参量估计范围：缩放因子s：0．71〈s〈1．40，旋转角度R〉30°，平移参量｜d｜〉35。算法在保证了高精度的同时，具有更为宽松的使用条件及适用性。

英文摘要：

To estimation the multi-parameter including scaling, rotation and translation, a robust globe motion estimation algorithm was proposed. In this algorithm, feature point matching is carried out through with feature curve matching, which mainly includes three steps： constructing feature block, creating feature curve and calculating feature curve correlation. Then, a linear system is constructed and solved by repeated least-squares to generate the global motion parameters with these matched feature point pairs. Experiments show that, the matching error of corresponding point pair is less than 5%, and the multi-parameter error is less than 0.1 pixel, 0.1degree, and 0.1% scales. The processing capability： scaling varying from 0.71 to 1.41, rotation factor R〉30 degree, translation factor ｜d｜〉35. The algorithm is robust and can be widely applied to global motion estimation field.