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Curve and surface fitting by implicit polynomials: Optimum degree finding and heuristic refinement

机译:隐式多项式的曲线和曲面拟合:最佳度查找和启发式细化

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Finding an implicit polynomial that fits a set of observations X is the goal of many researches in recent years. However, most existing algorithms assume the knowledge of the degree of the implicit polynomial that best represents the points. This paper presents two main contributions. First, a new distance measure between X and the implicit polynomial is defined. Second, this distance is used to define an algorithm able to find the degree of the polynomial needed for the representation of the data set. The proposed algorithm is based on the idea of gradually increase the degree, while there is an improvement in the smoothness of the solutions. The experiments confirm the validity of the approach for the selected 2D and 3D datasets. (C) 2017 Elsevier Ltd. All rights reserved.
机译:寻找适合一组观测值X的隐式多项式是近年来许多研究的目标。但是,大多数现有算法都假定隐式多项式的度最能代表这些点。本文提出了两个主要的贡献。首先,定义X与隐式多项式之间的新距离度量。其次,此距离用于定义一种算法,该算法能够找到表示数据集所需的多项式的次数。所提出的算法基于逐渐增加度数的思想,同时解决方案的平滑度有所提高。实验证实了该方法对所选2D和3D数据集的有效性。 (C)2017 Elsevier Ltd.保留所有权利。

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