传统非实时图像拼接方法易因局部图像失配导致全局拼接中断。此外，显微图像具有大量相似的微观结构，存在特征检测耗时长、误匹配率高等问题。为此，提出一种基于载物台运动信息的显微图像预测拼接算法。通过控制电动载物台XY轴移动距离来决定相邻图像间重叠区域大小，在图像的重叠区域采用加速稳健特征算法检测特征点。利用图像间前后位置关系预测待匹配特征点的范围，在预测范围内筛选出最小欧氏距离的待匹配点。最后通过匹配特征点对的斜率粗筛选匹配点对，随机抽样一致性算法进行精匹配并计算单应性矩阵配准图像完成拼接，使用改进的加权平均算法融合拼接图像。实验结果表明：与暴力匹配和快速最近邻搜索算法相比，所提算法匹配率提高7.95%~26.52%，有效提高配准精度。同时，当图像分辨率为1600×1200时，多图拼接速率为2 frame·s^-1，其效果优于AutoStitch软件拼接效果。

Traditional non-real-time image stitching methods can easily lead to global stitching interruption due to local image misalignment. In addition, microscopic images have numerous similar microstructures, causing problems such as long feature detection time and high misalignment rate. To address these issues, a microscopic image prediction stitching algorithm based on carrier stage motion information is proposed. First, the size of the overlapping area between adjacent images is determined by controlling the XY axis movement distance of the electric carrier stage. The accelerated robust feature algorithm is then used to detect feature points in the overlapping area of the image. Second, the range of feature points to be matched is predicted based on the position relationship of the images, and the feature point with the minimum Euclidean distance is selected within the predicted range for matching. Finally, matching point pairs are coarsely screened by the slope of the matching feature points, and precise matching is performed using the random sample consensus algorithm to calculate the homography matrix and complete the image stitching. The improved weighted average algorithm is used to fuse the stitched images. Experimental results show that the proposed algorithm achieves a superior matching rate improvement of 7.95% to 26.52% compared to those obtained via the brute force and fast library for approximate nearest neighbors algorithms, effectively improving the registration accuracy. Moreover, at a resolution of 1600×1200, the multi-image stitching rate of 2 frame·s^-1 achieves better results than those obtained by the AutoStitch software.