中文摘要：

提出了一种基于近邻主特征匹配的亚像素级位移测量方法.改进后的近邻主特征提取过程通过修正散度矩阵的构造,最大化相邻位移图像块投影距离,提髙了算法的精度和稳定性.通过将训练过程离线化,提出了基于近邻主特征匹配的微纳米位移测量算法,并通过仿真实验验证了图像块在不同大小和位置情况下算法的精度.在髙精度纳米平台、髙倍显微镜及标准栅格构成的系统中进行了多角度的实验,验证了算法的有效性.算法的测量精度比传统的图像块匹配方法提髙了近10倍,特别是算法对于图像块位置和大小的选择鲁棒性更髙.

英文摘要：

A new sub-pixel displacement measurement method is proposed based on the neighbor principal feature matching. The improved main features extraction process enhances the accuracy and stability of the algorithm by reconstructing divergence correction matrix and maximizing the distance of adjacent image blocks. The overall micro/nano scale measurement method is designed based on the neighbor principal feature matching by off-line training process,and the simulation verifies the accuracy of the method which is used for the image blocks with different sizes and positions. The high-precision nano platform,the high power microscope and the standard grid are used together to validate the measurement. The accuracy of the algorithm is increased by nearly 10 times compared with the conventional blocks matching method. Further,the algorithm has higher robustness in selecting the position and size of the image blocks.