Approximate conversion of spline curves
Computer Aided Geometric Design - Special issue: Topics in CAGD
Intrinsic parametrization for approximation
Computer Aided Geometric Design
Spline approximation of offset curves
Computer Aided Geometric Design
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Choosing nodes in parametric curve interpolation
Computer-Aided Design
A technique for smoothing scattered data with conic sections
Computers in Industry
Curves and surfaces for computer aided geometric design
Curves and surfaces for computer aided geometric design
Mathematical methods in computer aided geometric design
Mathematical methods in computer aided geometric design
A data dependent parametrization for spline approximation
Mathematical methods in computer aided geometric design
An introduction to the curves and surfaces of computer-aided design
An introduction to the curves and surfaces of computer-aided design
Parameter optimization in approximating curves and surfaces to measurement data
Computer Aided Geometric Design
Curve and surface design
Surface fitting to scattered data by a sum of Gaussians
Computer Aided Geometric Design
Digital image processing
Computer Aided Geometric Design
A vegetarian approach to optimal parameterizations
Computer Aided Geometric Design
The approximation power of moving least-squares
Mathematics of Computation
Global reparametrization for curve approximation
Computer Aided Geometric Design
Curve reconstruction from unorganized points
Computer Aided Geometric Design
Curve Fitting by a One-Pass Method With a Piecewise Cubic Polynomial
ACM Transactions on Mathematical Software (TOMS)
Computational Geometry for Design and Manufacture
Computational Geometry for Design and Manufacture
Curve reconstruction from noisy samples
Computational Geometry: Theory and Applications - Special issue on the 19th annual symposium on computational geometry - SoCG 2003
IEEE Computer Graphics and Applications
Fitting B-spline curves to point clouds by curvature-based squared distance minimization
ACM Transactions on Graphics (TOG)
A randomized knot insertion algorithm for outline capture of planar images using cubic spline
Proceedings of the 2007 ACM symposium on Applied computing
Constrained curve fitting on manifolds
Computer-Aided Design
Fitting curves and surfaces to point clouds in the presence of obstacles
Computer Aided Geometric Design
Curve reconstruction from noisy samples
Computational Geometry: Theory and Applications - Special issue on the 19th annual symposium on computational geometry - SoCG 2003
Computer Aided Geometric Design
Outline Capture of Images by Multilevel Coordinate Search on Cubic Splines
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A novel AR-based robot programming and path planning methodology
Robotics and Computer-Integrated Manufacturing
Haptic data compression based on curve reconstruction
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Haptic data compression based on quadratic curve reconstruction and prediction
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Interactively visualizing procedurally encoded scalar fields
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Robust reconstruction of 2D curves from scattered noisy point data
Computer-Aided Design
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Given a large set of irregularly spaced points in the plane, an algorithm for partitioning the points into subsets and fitting a parametric curve to each subset is described. The points could be measurements from a physical phenomenon, and the objective in this process could be to find patterns among the points and describe the phenomenon analytically. The points could be measurements from a geometric model, and the objective could be to reconstruct the model by a combination of parametric curves. The algorithm proposed here can be used in various applications, especially where given points are dense and noisy. Examples demonstrating the behavior of the algorithm under noise and density of the points are presented and discussed.