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Finite Sample Bias of Robust Scale Estimators in Computer Vision Problems

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

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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.

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References

  1. Bab-Hadiashar, A., Suter, D.: Robust Optic flow computation. Int. J. Computer Vision 29(1), 59–77 (1998)

    Article  Google Scholar 

  2. Meer, P.: Robust techniques for computer vision. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision, pp. 107–190. Prentice Hall, Englewood Cliffs (2004)

    Google Scholar 

  3. Torr, P.H.S.: Geometric motion segmentation and model selection. Philosophical Trans. of the Royal Society A, 1321–1340 (1998)

    Google Scholar 

  4. Yu, X., Bui, T.D., Krzyzak, A.: Robust estimation for range image segmentation and reconstruction. IEEE Trans. PAMI 16(5), 530–538 (1994)

    Google Scholar 

  5. Wang, H., Suter, D.: Robust adaptive-scale parametric model estimation for computer vision. IEEE Trans. PAMI 26(11), 1459–1474 (2004)

    Google Scholar 

  6. Bab-Hadiashar, A., Suter, D.: Robust segmentation of visual data using ranked unbiased scale estimator. ROBOTICA 17, 649–660 (1999)

    Article  Google Scholar 

  7. Stewart, C.V.: Bias in robust estimation caused by discontinuities and multiple structures. IEEE Trans. PAMI 19(8), 818–833 (1997)

    Google Scholar 

  8. Authors: Consistency analysis of robust scale estimators for multi-structural visual data. IEEE Trans. PAMI (submitted)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Swinburne University of Technology, Australia

    Reza Hoseinnezhad & Alireza Bab-Hadiashar

  2. Monash University, Australia

    David Suter

Authors
  1. Reza Hoseinnezhad
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  2. Alireza Bab-Hadiashar
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  3. David Suter
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Hoseinnezhad, R., Bab-Hadiashar, A., Suter, D. (2006). Finite Sample Bias of Robust Scale Estimators in Computer Vision Problems. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_45

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  • DOI: https://doi.org/10.1007/11919476_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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