Statistical bias of conic fitting and renormalization
Kenichi Kanatani · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1994
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.>