• DocumentCode
    639424
  • Title

    A Global Approach for the Detection of Vanishing Points and Mutually Orthogonal Vanishing Directions

  • Author

    Antunes, Mario ; Barreto, Joao P

  • Author_Institution
    Inst. of Syst. & Robot., Univ. of Coimbra, Coimbra, Portugal
  • fYear
    2013
  • fDate
    23-28 June 2013
  • Firstpage
    1336
  • Lastpage
    1343
  • Abstract
    This article presents a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions using lines detected in images of man-made environments. These two multi-model fitting problems are respectively cast as Uncapacited Facility Location (UFL) and Hierarchical Facility Location (HFL) instances that are efficiently solved using a message passing inference algorithm. We also propose new functions for measuring the consistency between an edge and a putative vanishing point, and for computing the vanishing point defined by a subset of edges. Extensive experiments in both synthetic and real images show that our algorithms outperform the state-of-the-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations that can either share one vanishing direction (Atlanta world) or be completely independent.
  • Keywords
    facility location; inference mechanisms; message passing; object detection; HFL; Manhattan-world configurations; UFL; global approach; hierarchical facility location; man-made environments; message passing inference algorithm; mutually orthogonal vanishing directions; putative vanishing point; uncapacited facility location; vanishing points detection; Calibration; Cameras; Computer vision; Estimation; Image edge detection; Image segmentation; Message passing; Facility Location; mutually orthogonal vanishing directions; vanishing point;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
  • Conference_Location
    Portland, OR
  • ISSN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2013.176
  • Filename
    6619020