Statistical bias of conic fitting and renormalization

Introducing a statistical model of noise in terms of the covariance matrix of the N-vector, we point out that the least-squares conic fitting is statistically biased. We present a new fitting scheme called renormalization for computing an unbiased estimate by automatically adjusting to noise. Relati...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 16; no. 3; pp. 320 - 326
Main Author Kanatani, K.
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.03.1994
IEEE Computer Society
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Covariance matrix
Curve fitting
Error analysis
Exact sciences and technology
Image analysis
Industrial relations
Pattern recognition. Digital image processing. Computational geometry
Power generation
Robotics and automation
Service robots
Testing
Working environment noise
Conic
Computer vision
Ellipse
Renormalization
Error analysis
Curve fitting
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Summary:Introducing a statistical model of noise in terms of the covariance matrix of the N-vector, we point out that the least-squares conic fitting is statistically biased. We present a new fitting scheme called renormalization for computing an unbiased estimate by automatically adjusting to noise. Relationships to existing methods are discussed, and our method is tested using real and synthetic data.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.276132