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SYSTEMS FOR GENERALIZING NON-UNIFORM RATIONAL B-SPLINE AND APPLICATION OF SYSTEMS

机译:广义非均匀有理B样条的系统及其应用

摘要

Systems and methods for generating approximations and other representations of data in a data set include a generalized non-uniform rational B-splines (NURBS) framework that facilitates optimized computer-generated representations having high accuracy and requiring less computing resources than previous frameworks capable of achieving similar accuracy. The framework includes a set of rational basis functions that define a mesh parametrization of the data set; these rational basis functions are based on the typical NURBS rational basis functions, but decoupled to provide discrete weights in each direction of a parametrized space. The value of each decoupled weight can be individually altered to improve the accuracy of the representation in the corresponding direction without altering the underlying mesh parametrization. The accuracy and efficiency of the proposed methods, particularly for data sets including discontinuities or localized gradients, is demonstrated through numerical experiments.
机译:用于生成数据集中的数据的近似值和其他表示的系统和方法包括广义非均匀有理B样条(NURBS)框架,该框架和方法能够比以前的框架实现具有高精度并且需要较少计算资源的优化的计算机生成的表示。相似的精度。该框架包括一组有理基础函数,这些函数定义了数据集的网格参数化;这些有理数基函数基于典型的NURBS有理数基函数,但解耦后可在参数化空间的每个方向上提供离散权重。每个解耦权重的值可以单独更改,以提高相应方向上的表示精度,而无需更改基础网格参数化。通过数值实验证明了所提出方法的准确性和效率,特别是对于包括不连续性或局部梯度的数据集。

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