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A Method of Image Registration Based On Best Similarity of Local Geometric Figure

机译:一种基于局部几何图形最佳相似性的图像配准方法

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Image Registration is an important part of computer vision. We propose a method of image registration by obtaining best similarity of local geometric figure that utilizes opposite core difference (OCD) of corresponding local figure. This method gets initial matching after describing precisely SIFT points by constructing feature subspaces based on the detection of SIFT feature points. Then we describe the corresponding similarity by OCD of local figure constructed by SIFT points and choose the feature points that possess highest similarity measure as point set to compute projective transformation matrix T_(opt) _( ). Experiments have proved that the precision of the matrix T_(opt) _(?) and the Image matching is at a high level.
机译:图像注册是计算机视觉的重要组成部分。我们通过获得利用相对的本地图形的相反核心差(OCD)的局部几何图的最佳相似性来提出一种图像登记方法。通过基于SIFT特征点的检测,通过构造特征子空间来描述精确的SIFT点之后,此方法获得初始匹配。然后,我们通过SIFT点构造的本地数据的OCD描述了相应的相似性,并选择具有最高相似度量的特征点,该指标设置为计算投影变换矩阵T_(OPT)_()。实验证明,矩阵T_(OPT)_(α)的精度和图像匹配是高电平的。

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