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张朝鑫, 席平. 高斯几何矩及其在特征匹配与图像配准中的应用[J]. 计算机辅助设计与图形学学报, 2014, 26(7): 1116-1125.
引用本文: 张朝鑫, 席平. 高斯几何矩及其在特征匹配与图像配准中的应用[J]. 计算机辅助设计与图形学学报, 2014, 26(7): 1116-1125.
Zhang Chaoxin, Xi Ping. Gaussian-Geometric Moments and Its Application in Feature Matching & Image Registration[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(7): 1116-1125.
Citation: Zhang Chaoxin, Xi Ping. Gaussian-Geometric Moments and Its Application in Feature Matching & Image Registration[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(7): 1116-1125.

高斯几何矩及其在特征匹配与图像配准中的应用

Gaussian-Geometric Moments and Its Application in Feature Matching & Image Registration

  • 摘要: 针对几何矩存在特征表述能力较弱和对噪声敏感等缺点,提出一种高斯几何矩,并给出了它的平移和旋转不变矩,同时提出一种基于高斯几何矩的图像配准方法.在几何矩的基础上引入高斯核,定义了高斯几何矩;再由高斯复数矩推导高斯几何矩的位移不变矩及旋转不变矩的独立与完备集;讨论了尺度因子对高斯几何矩表征图像的影响,充分利用尺度因子的灵活性特点,重点捕获图像的有效信息,提高了图像的特征表述能力.对高斯几何矩和几何矩的特征匹配能力与图像配准能力进行详细比较的结果表明,文中提出的高斯几何矩矩比几何矩具有更强的特征表述能力和抗噪声干扰能力.

     

    Abstract: In this paper,Gaussian-geometric moment(GGM) and their translation and rotation invariants have been introduced and a GGM-based image registration method has been proposed.Firstly,on the basis of GM,gauss kernel is employed to define the GGM;secondly,the GGM invariants are derived by means of the Gaussian-complex moment and an independent and complete set of GGM rotational invariants is given;finally,feature description capability of GGM by the influence of the scale parameter is fully studied.The capability of feature matching and image registration of both moments has been comprehensively tested by experiments,the results of which show that the proposed moments are much better than geometric ones in noise resistance and feature description.

     

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