Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56411
Type: Report
Title: Robust scale estimation from true parameters of model
Author: Wang, Hanzi
Suter, David
Publisher: Monash University
Issue Date: 2003
Series/Report no.: Technical report; MECSE-2-2003
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Hanzi Wang and David Suter
Abstract: In computer vision tasks, it frequently happens that gross noise and pseudo outliers occupy the absolute majority of the data. During the past several decades, a lot of robust estimators were developed to find parameters of a model from heavily contaminated data. However, correctly estimating the parameters of a model is not enough to differentiate inliers from outliers. Robust scale estimation is often needed as the postprocessing of most robust estimators followed by a weighted least squares method on the inliers. This paper shows that the scale estimation for most robust estimators is a very weak field and more work is needed. A more robust two-step scale estimator is presented and comparative experiments show its advantages over other available scale estimators
RMID: 0020094198
Published version: http://www.ecse.monash.edu.au/techrep/reports/
Appears in Collections:Computer Science publications

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