Abstract In this paper, we propose an adaptive knot placement algorithm for B-Spline curve approximation to dense and noisy 2D data points. The proposed algorithm is based on a heuristic rule for knot placement. It consists in constructing a distribution knot function by blending geometric criteria such as discrete derivatives, discrete angular variations and curvature. It has been successfully compared to three well known methods for approximating various noisy functions and sets of data in handwriting context.

中文翻译：

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B样条近似的一种新的确定性启发式结点放置

摘要 在本文中，我们提出了一种自适应结点放置算法，用于 B-Spline 曲线逼近密集和嘈杂的 2D 数据点。所提出的算法基于用于结放置的启发式规则。它包括通过混合几何标准（例如离散导数、离散角度变化和曲率）来构建分布结函数。它已成功地与三种众所周知的方法进行比较，用于在手写上下文中逼近各种噪声函数和数据集。