DocumentCode :
1510196
Title :
Direct least square fitting of ellipses
Author :
Fitzgibbon, Andrew ; Pilu, Maurizio ; Fisher, Robert B.
Author_Institution :
Dept. of Sci. Eng., Oxford Univ., UK
Volume :
21
Issue :
5
fYear :
1999
fDate :
5/1/1999 12:00:00 AM
Firstpage :
476
Lastpage :
480
Abstract :
This work 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-b2=1, the new method incorporates the ellipticity constraint into the normalization factor. The proposed method combines several advantages: It is ellipse-specific, so that even bad data will always return an ellipse. It can be solved naturally by a generalized eigensystem. It is extremely robust, efficient, and easy to implement
Keywords :
computational complexity; curve fitting; eigenvalues and eigenfunctions; least squares approximations; algebraic distance; computational expense; direct least square ellipse fitting; ellipticity constraint; general conics; generalized eigensystem; normalization factor; scattered data; Application software; Computer vision; Eigenvalues and eigenfunctions; Fitting; Iterative algorithms; Iterative methods; Least squares methods; Pattern recognition; Robustness; Scattering;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.765658
Filename :
765658
Link To Document :
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