Choosing nodes in parametric curve interpolation
Computer-Aided Design
Knot selection for parametric spline interpolation
Mathematical methods in computer aided geometric design
A survey of applications of an affine invariant norm
Mathematical methods in computer aided geometric design
Parameter optimization in approximating curves and surfaces to measurement data
Computer Aided Geometric Design
Fundamentals of computer aided geometric design
Fundamentals of computer aided geometric design
Global reparametrization for curve approximation
Computer Aided Geometric Design
A universal parametrization in B-spline curve and surface interpolation
Computer Aided Geometric Design
Curves and surfaces for CAGD: a practical guide
Curves and surfaces for CAGD: a practical guide
Discrete Curvature Based on Osculating Circle Estimation
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Universal parametrization in constructing smoothly-connected B-spline surfaces
Computer Aided Geometric Design
B-spline curve fitting based on adaptive curve refinement using dominant points
Computer-Aided Design
A local fitting algorithm for converting planar curves to B-splines
Computer Aided Geometric Design
Adaptive knot placement in B-spline curve approximation
Computer-Aided Design
Cubic B-spline curve approximation by curve unclamping
Computer-Aided Design
Local computation of curve interpolation knots with quadratic precision
Computer-Aided Design
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The parameterization method plays a critical role in B-spline interpolation. Some of the well-known parameterizations are the uniform, centripetal, chord length, Foley and universal methods. However, the interpolating results of these methods do not always satisfy all data features. In this study, we propose a new parameterization method which aims to improve the wiggle deviation of the interpolation, especially when interpolating the abrupt data interpolation. This new method is a refined centripetal method. The core of refinement is introducing the osculating circle at each data point. Besides the new parameterization method, we also design a fine wiggle validation method to verify the performance of all methods. In this paper, the proposed method is compared with centripetal, chord length, Foley, uniform and universal methods in both curve and surface cases. As a result, the proposed method has fewer wiggles than the centripetal method and other methods in the cases of abrupt-changing data. In addition, this refined method is stable for all kinds of data types, including free-form data distribution in this paper. The proposed method has fewer drawbacks than other methods, such as wiggles, oscillations, loops, and peaks, among others. More advantage, the proposed method is less influenced by the degree changing.