Robust ellipse and spheroid fitting

Jieqi Yu; Sanjeev R. Kulkarni; H. Vincent Poor

doi:10.1016/j.patrec.2011.11.025

Robust ellipse and spheroid fitting

Jieqi Yu, Sanjeev R. Kulkarni, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

Abstract

Ellipse and ellipsoid fitting has been extensively researched and has broad applications. Traditional ellipse fitting methods provide accurate estimation of ellipse parameters in the case of low noise. However, their performance is compromised when the noise level or the ellipse eccentricity are high. In this paper, an algorithm based on the geometric definition of an ellipse/spheroid (a special class of ellipsoid) is proposed. It performs well in high-noise, and high-eccentricity cases. The efficacy of the new algorithm is demonstrated through simulations.

Original language	English (US)
Pages (from-to)	492-499
Number of pages	8
Journal	Pattern Recognition Letters
Volume	33
Issue number	5
DOIs	https://doi.org/10.1016/j.patrec.2011.11.025
State	Published - Apr 1 2012

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

Keywords

Curve fitting
Parameter estimation
Surface fitting

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10.1016/j.patrec.2011.11.025

Cite this

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title = "Robust ellipse and spheroid fitting",

abstract = "Ellipse and ellipsoid fitting has been extensively researched and has broad applications. Traditional ellipse fitting methods provide accurate estimation of ellipse parameters in the case of low noise. However, their performance is compromised when the noise level or the ellipse eccentricity are high. In this paper, an algorithm based on the geometric definition of an ellipse/spheroid (a special class of ellipsoid) is proposed. It performs well in high-noise, and high-eccentricity cases. The efficacy of the new algorithm is demonstrated through simulations.",

keywords = "Curve fitting, Parameter estimation, Surface fitting",

author = "Jieqi Yu and Kulkarni, {Sanjeev R.} and Poor, {H. Vincent}",

note = "Funding Information: This research was supported in part by the US Office of Naval Research under Grant Nos. N00014-07-1-0555 and N00014-09-1-0342 , the US Army Research Office under Grant No. W911NF-07-1-0185 , and the National Science Foundation under Science & Technology Center grant CCF-0939370 . ",

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AU - Yu, Jieqi

AU - Kulkarni, Sanjeev R.

AU - Poor, H. Vincent

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Y1 - 2012/4/1

N2 - Ellipse and ellipsoid fitting has been extensively researched and has broad applications. Traditional ellipse fitting methods provide accurate estimation of ellipse parameters in the case of low noise. However, their performance is compromised when the noise level or the ellipse eccentricity are high. In this paper, an algorithm based on the geometric definition of an ellipse/spheroid (a special class of ellipsoid) is proposed. It performs well in high-noise, and high-eccentricity cases. The efficacy of the new algorithm is demonstrated through simulations.

AB - Ellipse and ellipsoid fitting has been extensively researched and has broad applications. Traditional ellipse fitting methods provide accurate estimation of ellipse parameters in the case of low noise. However, their performance is compromised when the noise level or the ellipse eccentricity are high. In this paper, an algorithm based on the geometric definition of an ellipse/spheroid (a special class of ellipsoid) is proposed. It performs well in high-noise, and high-eccentricity cases. The efficacy of the new algorithm is demonstrated through simulations.

KW - Curve fitting

KW - Parameter estimation

KW - Surface fitting

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 5

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Robust ellipse and spheroid fitting

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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