Algebraic Fitting of Quadric Surfaces to Data

TitleAlgebraic Fitting of Quadric Surfaces to Data
Publication TypeJournal Article
Year of Publication2005
AuthorsAl-Subaihi, I, Watson, GA
Volume9
Issue4
Start Page539
Pagination10
Date Published2005
ISSN1083-2564
AMS65D10, 65D32
Abstract

A important problem is that of finding a quadric surface which gives a “best” fit to m given data points. There are many application areas, for example metrology, computer graphics, pattern recognition, and in particular quadric surfaces are often to be found in manufactured parts. There are many criteria which can be used for fitting, but one of the simplest is so-called algebraic fitting, which exploits the fact that an expression for the curve can be given which is affine in the free parameters. Here we examine a general class of such algebraic fitting problems, consider how the members of the class can be interpreted in terms of the errors in the data, and present simple algorithms which apply to all of the problems.

URLhttp://www.acadsol.eu/en/articles/9/4/7.pdf
Refereed DesignationRefereed
Full Text

REFERENCES
[1] I. Al-Subaihi and G. A. Watson, The use of the l1 and l∞ norms in fitting parametric curves
and surfaces to data, Applied Numerical Analysis and Computational Mathematics, To Appear.
[2] A. Atieg and G. A. Watson, A class of methods for fitting a curve or surface to data by
minimizing the sum of squares of orthogonal distances, J. Comp. Appl. Math., 158 (2003), 277-296.
[3] S. J. Ahn, W. Rauh and H.-J. Warnecke, Best-fit of implicit surfaces and plane curves, In:
Mathematical Methods for Curves and Surfaces: Oslo 2000 (Ed-s: T. Lyche and L. L. Schumacker),
Vanderbilt University Press, 2001.
[4] W. Gander, G. H. Golub and R. Strebel, Fitting of circles and ellipses: least square solution,
BIT, 34 (1994), 556-577.
[5] H.-P. Helfrich and D. Zwick, A trust region method for implicit orthogonal distance regression,
Numer. Alg., 5 (1993), 535-545.
[6] H.-P. Helfrich and D. S. Zwick, l1 and l∞ fitting of geometric elements, In: Algorithms for
Approximation IV (Ed-s: J. Levesley, I. Anderson and J. C. Mason), University of Huddersfield(2002), 162-168.
[7] M. R. Osborne and G. A. Watson, An analysis of the total approximation problem in separable
norms, and an algorithm for the total l1 problem, SIAM J. Sci. Stat. Comp., 6 (1985), 410-424.
[8] L. Seufer and H. Sp¨ath, Least squares fitting of conic sections with type identification by
NURBS of degree two, Math. Comm., 4 (1999), 207-218.
[9] H. Sp¨ath, Orthogonal least squares fitting by conic sections, In: Recent Advances in Total Least
Squares and Errors-in-Variables Techniques (Ed. S. Van Huffel), SIAM, Philadelphia (1997),
259-264.
[10] H. Sp¨ath, Total least squares fitting with quadrics, Pure Appl. Maths., 11 (2004), 103-105.
[11] H. Sp¨ath and G. A. Watson, On orthogonal linear l1 approximation, Num. Math., 51 (1987),
531-543.
[12] G. Taubin, Estimation of planar curves, surfaces and nonplanar space curves defined by implicit
equations with applications to edge and range image segmentation, IEEE Trans. PAMI, 13
(1991), no. 11.
[13] D. A. Turner, The Approximation of Cartesian Co-ordinate Data by Parametric Orthogonal
Distance Regression, PhD Thesis, University of Huddersfield, 1999.
[14] J. van Tiel, Convex Analysis, John Wiley and Sons, Chichester, 1984.
[15] J. M. Varah, Least squares data fitting with implicit functions, BIT, 36 (1996), 842-854.
[16] G. A. Watson, On the Gauss-Newton method for l1 orthogonal distance regression, IMA J. of
Num. Anal., 22 (2002), 345-357.
[17] N. Werghi, R. B. Fisher, A. Ashbrook and C. Robertson, Faithful recovering of quadric surfaces
from 3D range data, In: Proc. 2nd Int. Conf. on 3-D Digital Imaging and Modeling, Ottawa,
Canada (1999), 280-289.