将带限曲线拟合到平面上各点的延续方法

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED Advances in Computational Mathematics Pub Date : 2024-07-16 DOI:10.1007/s10444-024-10144-5
Mohan Zhao, Kirill Serkh
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引用次数: 0

摘要

在本文中,我们描述了一种拟合解析和带限封闭或开放曲线的算法,用于插补 \(\mathbb {R}^{2}\) 中的任意点集合。其主要思想是通过迭代滤波弧长函数导数和曲线切线角度的傅里叶或切比雪夫系数来平滑曲线参数化,并在每一步滤波后应用平滑扰动,直到曲线由合理数量的系数表示为止。经过有限次数的迭代,该算法能生成一条通过点集的曲线,其精度达到机器精度。如果离散化节点数为 N,则每次迭代的运算量为 O(N log N)。所生成的曲线平滑、仿射不变、视觉效果好,不会出现任何振纹。所构建曲线的带宽远远小于以往方法所构建曲线的带宽。我们通过几个数值实验证明了我们算法的性能。
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A continuation method for fitting a bandlimited curve to points in the plane

In this paper, we describe an algorithm for fitting an analytic and bandlimited closed or open curve to interpolate an arbitrary collection of points in \(\mathbb {R}^{2}\). The main idea is to smooth the parametrization of the curve by iteratively filtering the Fourier or Chebyshev coefficients of both the derivative of the arc-length function and the tangential angle of the curve and applying smooth perturbations, after each filtering step, until the curve is represented by a reasonably small number of coefficients. The algorithm produces a curve passing through the set of points to an accuracy of machine precision, after a limited number of iterations. It costs O(N log N) operations at each iteration, provided that the number of discretization nodes is N. The resulting curves are smooth, affine invariant, and visually appealing and do not exhibit any ringing artifacts. The bandwidths of the constructed curves are much smaller than those of curves constructed by previous methods. We demonstrate the performance of our algorithm with several numerical experiments.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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