MURAL - Maynooth University Research Archive Library



    A New Hough Transform for the Detection of Arbitrary 3-Dimensional Objects


    McDonald, John and Vernon, David (1998) A New Hough Transform for the Detection of Arbitrary 3-Dimensional Objects. In: Proceedings of the Optical Engineers Society of Ireland and the Irish Machine Vision and Image Processing Joint Conference 1998. National University of Ireland Maynooth, pp. 243-255.

    [img]
    Preview
    Download (200kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    The existing Generalised Hough Transform, although altered to cater for scaling and rotation of the object in the plane, fails to detect the object under rotations out of the plane. This is due to the lack of 3-dimensional information contained in the 2-dimensional template image. In this paper we present a new Hough Transform, known as the Surface Normal Hough Transform (SNHT), which using a suitable 2-dimensional surface representation, transforms a set of surface normal to a surface parameter space. The effect of the SNHT is to map point sets representing a surface in the input space, to a peak in the parameter space. The coordinates of this peak parameterise the given surface and hence allow for post invariant object detection and localisation.

    Item Type: Book Section
    Keywords: 3-D Computer Vision; Hough Transform; Post Invariant Object Detection; Surface Registration;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8268
    Depositing User: John McDonald
    Date Deposited: 31 May 2017 15:02
    Publisher: National University of Ireland Maynooth
    Refereed: Yes
    URI:
      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

      Downloads per month over past year

      Origin of downloads