Learnt knot placement in B-spline curve approximation using support vector machines

DOI10.1016/j.cagd.2018.03.019zbMath1505.65028OpenAlexW2790242548WikidataQ130070564 ScholiaQ130070564MaRDI QIDQ1644366

Matthias O. Franz, Pascal Laube, Georg Umlauf

Publication date: 21 June 2018

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2018.03.019

zbMATH Keywords

support vector machineknot placementB-spline curve approximation

Mathematics Subject Classification ID

Numerical computation using splines (65D07) Learning and adaptive systems in artificial intelligence (68T05) Spline approximation (41A15) Computer-aided design (modeling of curves and surfaces) (65D17)

Related Items (5)

The unimodality of initial B-spline approximations in spline fitting ⋮ Knot Placement for B-Spline Curve Approximation via $L_{∞, 1}$-Norm and Differential Evolution Algorithm ⋮ Data approximation by \(L^1\) spline fits with free knots ⋮ A new deterministic heuristic knots placement for B-spline approximation ⋮ Knot calculation for spline fitting based on the unimodality property

Uses Software

• LIBSVM

Cites Work

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• Firefly algorithm for explicit B-spline curve fitting to data points
• Adaptive knot placement in B-spline curve approximation
• Knot removal for parametric B-spline curves and surfaces
• Choosing nodes in parametric curve interpolation
• Parameter optimization in approximating curves and surfaces to measurement data
• Surface approximation to scanned data
• Support Vector Machines for Classification of Geometric Primitives in Point Clouds
• A Data-Reduction Strategy for Splines with Applications to the Approximation of Functions and Data

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