使用归一化傅立叶描述子的三维轮廓形状匹配

Hao Zhang, E. Fiume
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引用次数: 18

摘要

在本文中,我们开发了一个简单的特征空间匹配算法,用于封闭的三维轮廓。我们的算法依赖于一种新的方法,该方法将三维轮廓的傅里叶描述子(FD)相对于其最低非零频率对应的两个FD系数进行归一化。剩下的匹配任务只涉及顶点移动和绕z轴旋转。我们的方法的灵感来自于观察,即一维信号的传统傅里叶变换相当于将信号分解为平滑算子的特征向量的线性组合。结果表明,我们的FD归一化相当于将逐步平滑的3D轮廓序列所接近的极限平面与xy平面对齐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shape matching of 3D contours using normalized Fourier descriptors
In this paper, we develop a simple, eigenspace matching algorithm for closed 3D contours. Our algorithm relies on a novel method which normalizes the Fourier descriptors (FDs) of a 3D contour with respect to two of its FD coefficients corresponding to the lowest non-zero frequencies. The remaining matching task only involves vertex shift and rotation about the z-axis. Our approach is inspired by the observation that the traditional Fourier transform of a 1D signal is equivalent to the decomposition of the signal into a linear combination of the eigenvectors of a smoothing operator. It turns out that our FD normalization is equivalent to aligning the limit plane approached by the sequence of progressively smoothed 3D contours with the xy-plane.
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