• DocumentCode
    3016908
  • Title

    Resolving the Generalized Bas-Relief Ambiguity by Entropy Minimization

  • Author

    Alldrin, Neil G. ; Mallick, Satya P. ; Kriegman, David J.

  • Author_Institution
    Univ. of California at San Diego, La Jolla
  • fYear
    2007
  • fDate
    17-22 June 2007
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    It is well known in the photometric stereo literature that uncalibrated photometric stereo, where light source strength and direction are unknown, can recover the surface geometry of a Lambertian object up to a 3-parameter linear transform known as the generalized bas relief (GBR) ambiguity. Many techniques have been proposed for resolving the GBR ambiguity, typically by exploiting prior knowledge of the light sources, the object geometry, or non-Lambertian effects such as specularities. A less celebrated consequence of the GBR transformation is that the albedo at each surface point is transformed along with the geometry. Thus, it should be possible to resolve the GBR ambiguity by exploiting priors on the albedo distribution. To the best of our knowledge, the only time the albedo distribution has been used to resolve the GBR is in the case of uniform albedo. We propose a new prior on the albedo distribution : that the entropy of the distribution should be low. This prior is justified by the fact that many objects in the real-world are composed of a small finite set of albedo values.
  • Keywords
    computational geometry; computer vision; inference mechanisms; minimisation; statistical distributions; stereo image processing; GBR ambiguity; GBR transformation; Lambertian object; albedo distribution; entropy minimization; generalized bas-relief ambiguity; intuitive reasoning; light source direction; light source strength; machine vision; surface geometry; uncalibrated photometric stereo; Entropy; Geometry; Image resolution; Light sources; Lighting; Machine vision; Photometry; Reflectivity; Snow; Surface reconstruction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1063-6919
  • Print_ISBN
    1-4244-1179-3
  • Electronic_ISBN
    1063-6919
  • Type

    conf

  • DOI
    10.1109/CVPR.2007.383208
  • Filename
    4270233