A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances.
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Publication:1410866
DOI10.1016/S0377-0427(03)00448-5zbMath1034.65007OpenAlexW2130967680MaRDI QIDQ1410866
Publication date: 15 October 2003
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0377-0427(03)00448-5
comparison of methodscurve fittingleast squares methodGauss-Newton methodsurface fittingorthogonal distances regression
Numerical smoothing, curve fitting (65D10) Computer-aided design (modeling of curves and surfaces) (65D17)
Related Items (8)
Efficient computation of the Gauss-Newton direction when fitting NURBS using ODR ⋮ Orthogonal least squares fitting with cylinders ⋮ Unified computation of strict maximum likelihood for geometric fitting ⋮ Fitting quadrics with a Bayesian prior ⋮ Application of gradient descent method to the sedimentary grain-size distribution fitting ⋮ Distance regression by Gauss-Newton-type methods and iteratively re-weighted least-squares ⋮ Incomplete orthogonal distance regression ⋮ Fitting parametric curves and surfaces by \(l_\infty\) distance regression
Cites Work
- A trust region method for implicit orthogonal distance regression
- Least-squares fitting of ellipses and hyperbolas
- A trust region algorithm for parametric curve and surface fitting
- Least-squares fitting by circles
- A Stable and Efficient Algorithm for Nonlinear Orthogonal Distance Regression
- Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola
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