• DocumentCode
    177009
  • Title

    Optimal line feature generation from low-level line segments under RANSAC framework

  • Author

    Li Haifeng ; Chen Rong

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Civil Aviation Univ. of China, Tianjin, China
  • fYear
    2014
  • fDate
    May 31 2014-June 2 2014
  • Firstpage
    4589
  • Lastpage
    4593
  • Abstract
    The low-level line segment features have low accuracy as they are more easily affected by the noise and differeent line segment detectors. Furthermore, the line segment is not a good feature for matching across multiple views when we need to finish the 3D reconstruction. However, the line feature is more robust for the noise. In this paper, a kind of line feature, ideal line, is defined and optimally estimated by clustering the line segments under RANSAC framework. The physical experiments are carried out to verify the proposed estimation method.
  • Keywords
    image reconstruction; image segmentation; maximum likelihood estimation; 3D reconstruction; RANSAC framework; ideal line; line segment detectors; low-level line segment features; optimal line feature generation; Educational institutions; Feature extraction; Image segmentation; Maximum likelihood estimation; Merging; Noise; Ideal Line; Line Segment; Maximum Likelihood Estimation; RANSAC;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (2014 CCDC), The 26th Chinese
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-3707-3
  • Type

    conf

  • DOI
    10.1109/CCDC.2014.6852992
  • Filename
    6852992