• DocumentCode
    1437411
  • Title

    Sign of Gaussian curvature from curve orientation in photometric space

  • Author

    Angelopoulou, Elli ; Wolff, Lawrence B.

  • Author_Institution
    GRASP Lab., Pennsylvania Univ., Philadelphia, PA, USA
  • Volume
    20
  • Issue
    10
  • fYear
    1998
  • fDate
    10/1/1998 12:00:00 AM
  • Firstpage
    1056
  • Lastpage
    1066
  • Abstract
    We compute the sign of Gaussian curvature using a purely geometric definition. Consider a point p on a smooth surface S and a closed curve γ on S which encloses p. The image of γ on the unit normal Gaussian sphere is a new curve β. The Gaussian curvature at p is defined as the ratio of the area enclosed by γ over the area enclosed by β as γ contracts to p. The sign of Gaussian curvature at p is determined by the relative orientations of the closed curves γ and β. We directly compute the relative orientation of two such curves from intensity data. We employ three unknown illumination conditions to create a photometric scatter plot. This plot is in one-to-one correspondence with the subset of the unit Gaussian sphere containing the mutually illuminated surface normal. This permits direct computation of the sign of Gaussian curvature without the recovery of surface normals. Our method is albedo invariant. We assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. Error analysis on simulated images shows the accuracy of our technique. We also demonstrate the performance of this methodology on empirical data
  • Keywords
    albedo; differential geometry; image reconstruction; photometry; reflectivity; Gaussian curvature; Gaussian sphere; closed curves; curve orientation; differential geometry; diffuse reflectance; illumination; photometric invariant; photometric space; relative orientation; shape recovery; Computational modeling; Contracts; Error analysis; Layout; Lighting; Photometry; Reflectivity; Scattering; Shape; Surface fitting;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.722615
  • Filename
    722615