STATISTICAL BIAS OF CONIC FITTING AND RENORMALIZATION

被引：96
作者：
KANATANI, K
机构：
[1] Department of Computer Science, Gunma University
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1994年 / 16卷 / 03期
关键词：
CONIC; ELLIPSE; CURVE FITTING; ERROR ANALYSIS; RENORMALIZATION;
D O I：
10.1109/34.276132
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Introducing a statistical model or 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.
引用
收藏
页码：320 / 326
页数：7
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