• DocumentCode
    2347115
  • Title

    Geometric distributions for catadioptric sensor design

  • Author

    Hicks, R. Andrew ; Perline, Ronald K.

  • Author_Institution
    Dept. of Math. & Comput. Sci., Drexel Univ., Philadelphia, PA, USA
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Abstract
    Catadioptric sensors are visual sensors that employ lenses (dioptrics) and mirrors (catoptrics). We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in. 3-dimensional space, which are generalizations of vector fields. The main idea is this: if one desires a reflective surface that will image the world in a certain way, then this condition determines the orientation of the tangent planes to the surface. Analytically, this means that the surface will then be determined by a pair of partial differential equations, which may or may not have a common solution. We show how to check if a common solution exists. If no common solution exists, we describe a method for obtaining optimal approximate solutions in a least-squares sense. As an example application, we construct a mirror that will give a panoramic view of a scene without any digital unwarping.
  • Keywords
    computer vision; geometry; image sensors; least squares approximations; mirrors; partial differential equations; 3-dimensional space; catadioptric sensor design; catoptrics; common solution; digital unwarping; dioptrics; geometric distributions; least squares approximation; lenses; mirror; optimal approximate solutions; panoramic view; partial differential equations; prescribed projections; reflective surface; tangent plane orientation; vector fields; visual sensors; Cameras; Computer science; Image sensors; Layout; Lenses; Mathematics; Mirrors; Partial differential equations; Sensor phenomena and characterization; Yagi-Uda antennas;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on
  • ISSN
    1063-6919
  • Print_ISBN
    0-7695-1272-0
  • Type

    conf

  • DOI
    10.1109/CVPR.2001.990526
  • Filename
    990526