IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-98661-2_85.html
   My bibliography  Save this book chapter

A Survey of Topology and Geometry-Constrained Segmentation Methods in Weakly Supervised Settings

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Ke Chen

    (University of Liverpool, Department of Mathematical Sciences, Centre for Mathematical Imaging Techniques)

  • Noémie Debroux

    (University of Clermont Auvergne, Pascal Institute)

  • Carole Le Guyader

    (Normandie University, INSA Rouen Normandie, Laboratory of Mathematics)

Abstract

Incorporating prior knowledge into a segmentation process— whether it be geometrical constraints such as landmarks to overcome the issue of weak boundary definition, shape prior knowledge or volume/area penalization, or topological prescriptions in order for the segmented shape to be homeomorphic to the initial one or to preserve the contextual relations between objects— proves to achieve more accurate results, while limiting human intervention. In this contribution, we intend to give an exhaustive overview of these so-called weakly/semi-supervised segmentation methods, following three main angles of inquiry: inclusion of geometrical constraints (landmarks, shape prior knowledge, volume/area penalization, etc.), incorporation of topological constraints (topology preservation enforcement, prescription of the number of connected components/holes, regularity enforcement on the evolving front, etc.), and, lastly, joint treatment of segmentation and registration that can be viewed as a special case of cosegmentation.

Suggested Citation

  • Ke Chen & Noémie Debroux & Carole Le Guyader, 2023. "A Survey of Topology and Geometry-Constrained Segmentation Methods in Weakly Supervised Settings," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 42, pages 1437-1482, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_85
    DOI: 10.1007/978-3-030-98661-2_85
    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:978-3-030-98661-2_85. 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.