IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-22687-8_18.html
   My bibliography  Save this book chapter

The Diverging Definition of Robustness in Statistics and Computer Vision

In: Robust and Multivariate Statistical Methods

Author

Listed:
  • Peter Meer

    (Rutgers University, Department of Electrical Engineering)

Abstract

Statistics and computer vision have a different role for robustness. Statisticians are primarily concerned with the theoretical properties of estimators when models are only approximately true. In computer vision, performance takes precedence over theoretical considerations. This divergence is exemplified in statistics by the robust M-estimator in contrast to the RANdom SAmple Consensus (RANSAC) and the Multiple Input Structures with Robust Estimator (MISRE) in computer vision. All three have defined algorithms, but the M-estimator is based on theoretical results, while RANSAC and MISRE only emphasize recovering significant inlier structures. I offer suggestions for how the theory of the M-estimator can be further applied to MISRE, or MISRE can be applied to M-estimator.

Suggested Citation

  • Peter Meer, 2023. "The Diverging Definition of Robustness in Statistics and Computer Vision," Springer Books, in: Mengxi Yi & Klaus Nordhausen (ed.), Robust and Multivariate Statistical Methods, pages 395-406, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-22687-8_18
    DOI: 10.1007/978-3-031-22687-8_18
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-031-22687-8_18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.