Robust fitting of ellipses and spheroids

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

doi:10.1109/ACSSC.2009.5470160

Robust fitting of ellipses and spheroids

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

Ellipse and ellipsoid fitting has been extensively researched and widely applied. Although traditional fitting methods provide accurate estimation of ellipse parameters in the lownoise case, their performance is compromised when the noise level or the ellipse eccentricity are high. A series of robust fitting algorithms are proposed that perform well in high-noise, higheccentricity ellipse/spheroid (a special class of ellipsoid) cases. The new algorithms are based on the geometric definition of an ellipse/spheroid, and improved using global statistical properties of the data. The efficacy of the new algorithms is demonstrated through simulations.

Original language	English (US)
Title of host publication	Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages	94-98
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2009.5470160
State	Published - 2009
Event	43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 1 2009 → Nov 4 2009

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	43rd Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	11/1/09 → 11/4/09

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2009.5470160

Cite this

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title = "Robust fitting of ellipses and spheroids",

abstract = "Ellipse and ellipsoid fitting has been extensively researched and widely applied. Although traditional fitting methods provide accurate estimation of ellipse parameters in the lownoise case, their performance is compromised when the noise level or the ellipse eccentricity are high. A series of robust fitting algorithms are proposed that perform well in high-noise, higheccentricity ellipse/spheroid (a special class of ellipsoid) cases. The new algorithms are based on the geometric definition of an ellipse/spheroid, and improved using global statistical properties of the data. The efficacy of the new algorithms is demonstrated through simulations.",

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

year = "2009",

doi = "10.1109/ACSSC.2009.5470160",

language = "English (US)",

isbn = "9781424458271",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "94--98",

booktitle = "Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers",

}

Yu, J, Kulkarni, SR & Poor, HV 2009, Robust fitting of ellipses and spheroids. in Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers., 5470160, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 94-98, 43rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 11/1/09. https://doi.org/10.1109/ACSSC.2009.5470160

TY - GEN

T1 - Robust fitting of ellipses and spheroids

AU - Yu, Jieqi

AU - Kulkarni, Sanjeev R.

AU - Poor, H. Vincent

PY - 2009

Y1 - 2009

N2 - Ellipse and ellipsoid fitting has been extensively researched and widely applied. Although traditional fitting methods provide accurate estimation of ellipse parameters in the lownoise case, their performance is compromised when the noise level or the ellipse eccentricity are high. A series of robust fitting algorithms are proposed that perform well in high-noise, higheccentricity ellipse/spheroid (a special class of ellipsoid) cases. The new algorithms are based on the geometric definition of an ellipse/spheroid, and improved using global statistical properties of the data. The efficacy of the new algorithms is demonstrated through simulations.

AB - Ellipse and ellipsoid fitting has been extensively researched and widely applied. Although traditional fitting methods provide accurate estimation of ellipse parameters in the lownoise case, their performance is compromised when the noise level or the ellipse eccentricity are high. A series of robust fitting algorithms are proposed that perform well in high-noise, higheccentricity ellipse/spheroid (a special class of ellipsoid) cases. The new algorithms are based on the geometric definition of an ellipse/spheroid, and improved using global statistical properties of the data. The efficacy of the new algorithms is demonstrated through simulations.

UR - http://www.scopus.com/inward/record.url?scp=77953832441&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77953832441&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2009.5470160

DO - 10.1109/ACSSC.2009.5470160

M3 - Conference contribution

AN - SCOPUS:77953832441

SN - 9781424458271

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 94

EP - 98

BT - Conference Record - 43rd Asilomar Conference on Signals, Systems and Computers

T2 - 43rd Asilomar Conference on Signals, Systems and Computers

Y2 - 1 November 2009 through 4 November 2009

ER -

Robust fitting of ellipses and spheroids

Abstract

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