A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances.

DOI10.1016/S0377-0427(03)00448-5zbMath1034.65007OpenAlexW2130967680MaRDI QIDQ1410866

A. Atieg, G. Alistair Watson

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

zbMATH Keywords

comparison of methodscurve fittingleast squares methodGauss-Newton methodsurface fittingorthogonal distances regression

Mathematics Subject Classification ID

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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