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|Title:||Robust scale estimation from true parameters of model|
|Series/Report no.:||Technical report; MECSE-2-2003|
|School/Discipline:||School of Computer Science|
|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|
|Appears in Collections:||Computer Science publications|
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