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A new deterministic heuristic knots placement for B-Spline approximation

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  • Michel, D.
  • Zidna, A.

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.

Suggested Citation

  • Michel, D. & Zidna, A., 2021. "A new deterministic heuristic knots placement for B-Spline approximation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 91-102.
    • Handle: RePEc:eee:matcom:v:186:y:2021:i:c:p:91-102
    DOI: 10.1016/j.matcom.2020.07.021
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    References listed on IDEAS

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    1. Idais, H. & Yasin, M. & Pasadas, M. & González, P., 2019. "Optimal knots allocation in the cubic and bicubic spline interpolation problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 164(C), pages 131-145.
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    Cited by:

    1. Saillot, Mathis & Michel, Dominique & Zidna, Ahmed, 2024. "B-spline curve approximation with transformer neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 223(C), pages 275-287.

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