基于改进SURF算法的显微图像大范围拼接
Large Scale Microscopic Image Mosaic Based on SURF Algorithm

DOI: 10.12677/CSA.2021.112030, PP. 299-304

Keywords: 大范围测量，图像拼接，SURF算法，Harris角点，加权融合
Large Scale Measurement, Image Mosaic, SURF Algorithm, Harris Corner, Weighted Fusion

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Abstract:

在实现大范围微结构测量时，白光干涉表面测量受限于单个光学视场的大小，不能看到完整的结构。为了获得大范围的视野效果，文中提出基于改进SURF算法对显微图像进行拼接。首先用Harris对图像进行特征点检测，然后对特征点进行描述符计算寻找点对之间的对应点对。再使用RANSAC对误配的点对进行删除，最后使用加权融合的方法来消除拼接缝。实验结果表明，该方法是有效的，可以将多次采集得到多幅图片拼成一幅完整的图片来实现大视野的测量。
In large-scale microstructure measurement, the white light interferometric surface measurement is limited by the size of a single optical field of view, and cannot see the complete structure. In order to obtain a wide range of visual field effect, this paper proposes an improved surf algorithm for micro-scopic image mosaic. Firstly, Harris is used to detect the feature points of the image, and then the descriptor of the feature points is calculated to find the corresponding point pairs. Then RANSAC is used to delete the mismatched point pairs, and the weighted fusion method is used to eliminate the seam. The experimental results show that the method is effective, and it can put multiple images collected many times into a complete image to realize the measurement of large field of vision.

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