MURAL - Maynooth University Research Archive Library

    Generalised relaxed Radon transform (GR2T) for robust inference

    Dahyot, Rozenn and Ruttle, Jonathan (2013) Generalised relaxed Radon transform (GR2T) for robust inference. Pattern Recognition, 46 (3). pp. 788-794. ISSN 0031-3203

    Download (213kB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    This paper introduces the generalised relaxed Radon transform (GR2T) as an extension to the generalised radon transform (GRT) [1]. This new modelling allows us to define a new framework for robust inference. The resulting objective functions are probability density functions that can be chosen differentiable and that can be optimised using gradient methods. One of this cost function is already widely used in the forms of the Hough transform and generalised projection based M-estimator, and it is interpreted as a conditional density function on the latent variables of interest. In addition the joint density function of the latent variables is also proposed as a cost function and it has the advantage of including a prior about the latent variable. Several applications, including lines detection in images and volume reconstruction from silhouettes captured from multiple views, are presented to underline the versatility of this framework.

    Item Type: Article
    Keywords: Generalised Radon transform; Hough transform; Robust inference; M-estimator; Generalised projection based M-estimator;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 15119
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 14 Dec 2021 15:13
    Journal or Publication Title: Pattern Recognition
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads