Finite Sample Bias of Robust Scale Estimators in Computer Vision Problems

  • Reza Hoseinnezhad
  • Alireza Bab-Hadiashar
  • David Suter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


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.


Sample Bias Finite Sample Robust Estimator Scale Estimate Residual Density 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Reza Hoseinnezhad
    • 1
  • Alireza Bab-Hadiashar
    • 1
  • David Suter
    • 2
  1. 1.Swinburne University of TechnologyAustralia
  2. 2.Monash UniversityAustralia

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