• DocumentCode
    253633
  • Title

    Finding Vanishing Points via Point Alignments in Image Primal and Dual Domains

  • Author

    Lezama, Jose ; Grompone von Gioi, Rafael ; Randall, Gregory ; Morel, Jean-Michel

  • fYear
    2014
  • fDate
    23-28 June 2014
  • Firstpage
    509
  • Lastpage
    515
  • Abstract
    We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection. The very same point alignment detection algorithm is used twice: First in the image domain to group line segment endpoints into more precise lines. Second, it is used in the dual domain where converging lines become aligned points. The use of the recently introduced PClines dual spaces and a robust point alignment detector leads to a very accurate algorithm. Experimental results on two public standard datasets show that our method significantly advances the state-of-the-art in the Manhattan world scenario, while producing state-of-the-art performances in non-Manhattan scenes.
  • Keywords
    object detection; Manhattan world scenario; PClines dual spaces; automatic vanishing point detection; dual domains; dual point alignment detection; group line segment endpoints; image primal domains; nonManhattan scenes; public standard datasets; Cameras; Clustering algorithms; Detectors; Estimation; Image segmentation; Three-dimensional displays; Transforms; 2d point alignments; line-to-point mapping; vanishing point detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
  • Conference_Location
    Columbus, OH
  • Type

    conf

  • DOI
    10.1109/CVPR.2014.72
  • Filename
    6909466