arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1903.03095math.NAcs.NA
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具有不连续核的形状驱动插值:误差分析、边缘提取及在MPI中的应用

Shape-Driven Interpolation with Discontinuous Kernels: Error Analysis, Edge Extraction and Applications in MPI

Università di Padova(帕多瓦大学);Università di Milano - Bicocca(米兰比可卡大学)

Stefano De Marchi, Wolfgang Erb, Francesco Marchetti, Emma Perracchione, Milvia Rossini

AI总结:

本文研究了一种基于RBF的散乱数据插值方法,通过变量缩放函数引入不连续性,分析了核函数的本原空间和误差界,并利用机器学习分类算法提取不连续性位置,通过数值实验验证了理论收敛率,并展示了在磁流体成像中的应用前景。

摘要:

准确的插值和近似技术对于具有不连续性的函数是许多应用中的关键工具,例如医学成像。本文研究了一种针对散乱数据插值的RBF类型方法,通过变量缩放函数引入不连续性。为了构建不连续基函数的核函数,需要有关插值函数边缘的信息。我们表征了由这些核函数张成的本原空间,并研究了节点集的填充距离的误差界。为了提取不连续性的位置,我们使用基于机器学习分类算法的分割方法。所进行的数值实验验证了在不连续性已知的情况下理论推导出的收敛速率。此外,对磁流体成像中的插值应用表明,所提出的方法非常有前景。

英文摘要:

Accurate interpolation and approximation techniques for functions with discontinuities are key tools in many applications as, for instance, medical imaging. In this paper, we study an RBF type method for scattered data interpolation that incorporates discontinuities via a variable scaling function. For the construction of the discontinuous basis of kernel functions, information on the edges of the interpolated function is necessary. We characterize the native space spanned by these kernel functions and study error bounds in terms of the fill distance of the node set. To extract the location of the discontinuities, we use a segmentation method based on a classification algorithm from machine learning. The conducted numerical experiments confirm the theoretically derived convergence rates in case that the discontinuities are a priori known. Further, an application to interpolation in magnetic particle imaging shows that the presented method is very promising.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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