• DocumentCode
    1747627
  • Title

    Theoretic design of a smart vision sensor

  • Author

    Gao, Hongmei ; Chen, Xiang

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1223
  • Abstract
    The correlation method for image analysis is applied to the theoretic design of a smart vision sensor, which can be implemented to find the 2-dimensional position of static targets. In particular, the problem of finding the deviations along both X and Y directions is formulated as a matching process between the saved template of the desired position and the pictures of real static targets captured by a `vision´ element (for example, a CCD camera) through correlation analysis of the spatial shifts in 2-dimension. It is noted that the size of the captured images by the vision element can be reduced in our design in order to accommodate fast real-time application with reasonable accuracy and reliability. An example shows that our design idea exhibits a good performance in finding the deviations along both X and Y axes of a sheet of foam barriers and can be potentially used to design a real high performance and cost saving smart vision sensor
  • Keywords
    correlation methods; image processing; image sensors; intelligent sensors; probability; 2D position; CCD camera; accuracy; correlation analysis; correlation method; cumulative probability density function; fast real-time application; foam barriers; gray level transformation; image analysis; image size; matching process; performance; reliability; smart vision sensor; spatial shifts; static targets; template; theoretic design; vision element; Cameras; Correlation; Design methodology; Hardware; Image analysis; Image sensors; Intelligent sensors; Optical sensors; Position measurement; Robotic assembly;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering, 2001. Canadian Conference on
  • Conference_Location
    Toronto, Ont.
  • ISSN
    0840-7789
  • Print_ISBN
    0-7803-6715-4
  • Type

    conf

  • DOI
    10.1109/CCECE.2001.933616
  • Filename
    933616