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首页> 外文会议>International Conference on Computer Application and System Modeling >Fast normalized cross-correlation image matching based on multiscale edge information
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Fast normalized cross-correlation image matching based on multiscale edge information

机译：基于多尺度边缘信息的快速标准化互相关图像匹配

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摘要

In order to overcome the Large computation of cross correlation matching, we propose a method of quick cross correlation matching. In traditional cross correlation matching all the pixels take part in computing, so the speed of matching is slow down. In this paper, we use multi-scale edge information which was extracted by improved Laplacian pyramid(ILP) as feather temple; to convert the gray information matching to the feature information matching; take advantage of the feature template and the image to be matched to establish indirect similarity measure to achieve speed-up, anti-geometric distortion. The multi-scale features of the method can meet multiple needs of the matching accuracy and speed. Experiments show, the method improves the speed of cross correlation matching, and has certain robustness.
机译：为了克服跨相关匹配的大计算，我们提出了一种快速交叉相关匹配的方法。在传统的交叉相关匹配中，所有像素都参与计算，因此匹配的速度速度慢。在本文中，我们使用由改进的Laplacian金字塔（ILP）提取的多尺度边缘信息作为羽毛寺;将灰色信息转换为特征信息匹配;利用要素模板和要匹配的图像来建立间接相似度测量以实现加速，防几何失真。该方法的多尺度特征可以满足匹配精度和速度的多种需求。实验表明，该方法提高了交叉相关匹配的速度，具有一定的鲁棒性。

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