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Statistical Bias of Conic Fitting and Renormalization
March 1994 (vol. 16 no. 3)
pp. 320-326

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.

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Index Terms:
image processing; curve fitting; statistical analysis; least squares approximations; matrix algebra; interference (signal); renormalisation; statistical bias; curve fitting; renormalization; statistical model; noise; covariance matrix; vector; least-squares conic fitting
Citation:
K. Kanatani, "Statistical Bias of Conic Fitting and Renormalization," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 3, pp. 320-326, March 1994, doi:10.1109/34.276132
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