Title :
Unbiased estimation of ellipses by bootstrapping
Author :
Cabrera, Javier ; Meer, Peter
Author_Institution :
Dept. of Stat., Rutgers Univ., Piscataway, NJ, USA
fDate :
7/1/1996 12:00:00 AM
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 :
computer vision; curve fitting; feature extraction; bias; bootstrapping; ellipses; error distribution; nonlinear estimators; unbiased estimation; Computer vision; Curve fitting; Data mining; Euclidean distance; Feature extraction; Image segmentation; Lattices; Senior members; Solid modeling; Symmetric matrices;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on