• DocumentCode
    2853454
  • Title

    Affine-permutation symmetry: invariance and shape space

  • Author

    Sepiashvili, David ; Moura, José M E ; Ha, Wctor H S

  • Author_Institution
    Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    2003
  • fDate
    28 Sept.-1 Oct. 2003
  • Firstpage
    307
  • Lastpage
    310
  • Abstract
    Studying similarity of objects by looking at their shapes arises naturally in many applications. However, under different viewpoints one and the same object appears to have different shapes. In addition, the correspondences between their feature points are unknown to the viewer. In this paper, we introduce the concept of intrinsic shape of an object that is invariant to affine-permutation shape distortions. We study geometry of the intrinsic shape space in the framework of differentiable manifolds with the emphasis on the computational aspects. We represent the intrinsic shape space as a folded Grassmann manifold. This allows us to easily analyze and compare different intrinsic shapes under the affine-permutation distortion without explicitly computing and recovering these intrinsic shapes. We present the mathematical equations for connecting two intrinsic shapes by a geodesic, measuring their similarity, and morphing one intrinsic shape onto another.
  • Keywords
    computational geometry; image morphing; affine-permutation shape distortions; affine-permutation symmetry; differentiable manifolds; folded Grassmann manifold; geodesic; intrinsic shape; invariance; mathematical equations; morphing; shape space; Application software; Equations; Geometry; Geophysics computing; Image processing; Image sensors; Joining processes; Level measurement; Object detection; Shape measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Statistical Signal Processing, 2003 IEEE Workshop on
  • Print_ISBN
    0-7803-7997-7
  • Type

    conf

  • DOI
    10.1109/SSP.2003.1289406
  • Filename
    1289406