• DocumentCode
    1470405
  • Title

    Polarization-based material classification from specular reflection

  • Author

    Wolff, Lawrence B.

  • Author_Institution
    Dept. of Comput. Sci., Columbia Univ., New York, NY, USA
  • Volume
    12
  • Issue
    11
  • fYear
    1990
  • fDate
    11/1/1990 12:00:00 AM
  • Firstpage
    1059
  • Lastpage
    1071
  • Abstract
    A computationally simple yet powerful method for distinguishing metal and dielectric material surfaces from the polarization characteristics of specularly reflected light is introduced. The method is completely passive, requiring only the sensing of transmitted radiance of reflected light through a polarizing filter positioned in multiple orientations in front of a camera sensor. Precise positioning of lighting is not required. An advantage of using a polarization-based method for material classification is its immunity to color variations, which so commonly exist on uniform material samples. A simple polarization-reflectance model, called the Fresnel reflectance model, is developed. The fundamental assumptions are that the diffuse component of reflection is completely unpolarized and that the polarization state of the specular component of reflection is dictated by the Fresnel reflection coefficients. The material classification method presented results axiomatically from the Fresnel reflectance model, by estimating the polarization Fresnel ratio. No assumptions are required about the functional form of the diffuse and specular components of reflection. The method is demonstrated on some common objects consisting of metal and dielectric parts
  • Keywords
    computer vision; computerised pattern recognition; computerised picture processing; light polarisation; printed circuit testing; reflectometry; Fresnel reflectance model; PC testing; dielectric material surfaces; material classification; metal surfaces; passive sensing; polarization Fresnel ratio; polarization-reflectance model; polarizing filter; specular reflection; transmitted radiance; Cameras; Computer vision; Conducting materials; Dielectric materials; Fresnel reflection; Inspection; Machine vision; Optical polarization; Optical reflection; Passive filters; Printed circuits; Reflectivity; Sensor phenomena and characterization;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.61705
  • Filename
    61705