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Type: Conference paper
Title: Finite sample bias of robust scale estimators in computer vision problems.
Author: Hoseinnezhad, R.
Bab-Hadiashar, A.
Suter, D.
Citation: Advances in visual computing: Second International Symposium, ISVC 2006 Lake Tahoe, NV, USA, November 6-8, 2006 Proceedings, Part I / George Bebis et al (eds.): pp.445-454
Publisher: Springer Verlag
Publisher Place: Berlin
Issue Date: 2006
Series/Report no.: Lecture Notes in Computer Science ; 4291
ISBN: 3540486283
ISSN: 0302-9743
Conference Name: International Symposium on Visual Computing (2nd : 2006 : Lake Tahoe)
Editor: Bebis, G.
Boyle, R.
Parvin, B.
Koracin, D.
Remagnino, P.
Nefian, A.
Meenakshisundaram, G.
Pascucci, V.
Zara, J.
Molineros, J.
Theisel, H.
Malzbender, T.
Statement of
Reza Hoseinnezhad, Alireza Bab-Hadiashar and David Suter
Abstract: In computer vision applications of robust estimation techniques, it is usually assumed that a large number of data samples are available. As a result, the finite sample bias of estimation processes has been overlooked. This is despite the fact that many asymptotically unbiased estimators have substantial bias in cases where a moderate number of data samples are available. Such cases are frequently encountered in computer vision practice, therefore, it is important to choose the right estimator for a given task by virtue of knowing its finite sample bias. This paper investigates the finite sample bias of robust scale estimation and analyses the finite sample performance of three modern robust scale estimators (Modified Statistical Scale Estimator, Residual Consensus estimator and Two-Step Scale Estimator) that have been used in computer vision applications. Simulations and real data experiments are used to verify the results.
Description: © Springer-Verlag Berlin Heidelberg 2006
DOI: 10.1007/11919476
Appears in Collections:Aurora harvest 5
Computer Science publications

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