• DocumentCode
    639427
  • Title

    Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?

  • Author

    Bergamasco, Filippo ; Albarelli, Andrea ; Rodola, Emanuele ; Torsello, Andrea

  • Author_Institution
    Dipt. di Sci. Ambientali, Inf. e Statistica, Univ. Ca´ Foscari Venezia, Venice, Italy
  • fYear
    2013
  • fDate
    23-28 June 2013
  • Firstpage
    1391
  • Lastpage
    1398
  • Abstract
    Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process. These considerations led to the definition of several ad hoc models that best adapt to different imaging devices, ranging from pinhole cameras with no radial distortion to the more complex catadioptric or polydioptric optics. In this paper we propose the use of an unconstrained model even in standard central camera settings dominated by the pinhole model, and introduce a novel calibration approach that can deal effectively with the huge number of free parameters associated with it, resulting in a higher precision calibration than what is possible with the standard pinhole model with correction for radial distortion. This effectively extends the use of general models to settings that traditionally have been ruled by parametric approaches out of practical considerations. The benefit of such an unconstrained model to quasi-pinhole central cameras is supported by an extensive experimental validation.
  • Keywords
    calibration; cameras; image processing; ad hoc models; fully unconstrained imaging model; image formation model; imaging devices; novel calibration approach; quasi-pinhole central camera model; radial distortion; standard central camera settings; Calibration; Cameras; Computational modeling; Estimation; Mathematical model; Three-dimensional displays; Camera Calibration; General Camera Model; Raxels;
  • 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.2013.183
  • Filename
    6619027