Unbiased estimation of ellipses by bootstrapping
- 1 July 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 18 (7) , 752-756
- https://doi.org/10.1109/34.506797
Abstract
A general method for eliminating the bias of nonlinear estimators using bootstrap is presented. Instead of the traditional mean bias we consider the definition of bias based on the median. The method is applied to the problem of fitting ellipse segments to noisy data. No assumption beyond being independent identically distributed is made about the error distribution and experiments with both synthetic and real data prove the effectiveness of the technique.Keywords
This publication has 18 references indexed in Scilit:
- Quantitative evaluation of performance through bootstrapping: edge detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Simulation methods for mean and median bias reduction in parametric estimationJournal of Statistical Planning and Inference, 1997
- A note on the least squares fitting of ellipsesPattern Recognition Letters, 1993
- An Introduction to the BootstrapPublished by Springer Nature ,1993
- Ellipse detection and matching with uncertaintyImage and Vision Computing, 1992
- Accurate parameter estimation of quadratic curves from grey-level imagesCVGIP: Image Understanding, 1991
- Multivariate Adaptive Regression SplinesThe Annals of Statistics, 1991
- Bootstrap Techniques for Error EstimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1987
- Measurement Error ModelsPublished by Wiley ,1987
- Fitting conic sections to scattered dataComputer Graphics and Image Processing, 1979