Parameter-free selective segmentation with convex variational methods

Spencer, Jack and Chen, Ke and Duan, Jinming (2019) Parameter-free selective segmentation with convex variational methods. IEEE Transactions on Image Processing, 28 (5). pp. 2163-2172. ISSN 1057-7149 (https://doi.org/10.1109/TIP.2018.2883521)

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Abstract

Selective segmentation methods involve incorporating user input to partition an image into a foreground and background. These methods are often sensitive to some aspect of the user input in a counter intuitive manner, making their use in practice difficult. The most robust methods often involve laborious refinement on the part of the user, and sometimes editing/supervision. The proposed method reduces the burden of the user by simplifying the requirements in the input. Specifically, the fitting term does not depend on a distance function, and so no selection parameter is introduced. Instead, we consider how the user input relates to some general intensity fitting term to ensure the approach is less sensitive to the decisions or intuition of the user. We give comparisons to existing approaches to show the advantages of the new selective segmentation model.

  • Item type:Article
    ID code:87431
    Dates:
    Date
    Event
    31 May 2019
    Published
    28 November 2018
    Published Online
    15 November 2018
    Accepted
    7 February 2018
    Submitted
    Notes:Copyright © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:23 Nov 2023 11:38
    Last modified:25 Apr 2024 01:37
    URI:https://strathprints.strath.ac.uk/id/eprint/87431
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