Direct least squares fitting of ellipses

This paper presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b/sup 2/=1 the new method incorporates the ellipticity constraint...

Full description

Saved in:
Bibliographic Details
Published inProceedings of 13th International Conference on Pattern Recognition Vol. 1; pp. 253 - 257 vol.1
Main Authors Fitzgibbon, A.W., Pilu, M., Fisher, R.B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Artificial intelligence
Computer vision
Fitting
Iterative algorithms
Iterative methods
Least squares methods
Noise robustness
Pattern recognition
Resilience
Scattering
Online AccessGet full text

Cover

More Information
Summary:This paper presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4ac-b/sup 2/=1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: 1) it is ellipse-specific so that even bad data will always return an ellipse; 2) it can be solved naturally by a generalized eigensystem, and 3) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other nonellipse-specific approaches would fail or require computationally expensive iterative refinements.
ISBN:9780818672828
081867282X
ISSN:1051-4651
DOI:10.1109/ICPR.1996.546029