• DocumentCode
    2116088
  • Title

    Efficient anisotropic α-Kernels decompositions and flows

  • Author

    Feigin, Micha ; Sochen, Nir ; Vemuri, Baba C.

  • Author_Institution
    Sch. of Math., Tel Aviv Univ., Tel Aviv
  • fYear
    2008
  • fDate
    23-28 June 2008
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    The Laplacian raised to fractional powers can be used to generate scale spaces as was shown in recent literature. This was later extended for inhomogeneous diffusion processes and more general functions of the Laplacian and studied for the Perona-Malik case. In this paper we extend the results to the truly anisotropic Beltrami flow. We additionally introduce a technique for splitting up the work into smaller patches of the image which greatly reduce the computational complexity and allow for the parallelization of the algorithm. Important issues involved in the numerical implementation are discussed.
  • Keywords
    computational complexity; graph theory; image segmentation; image sequences; parallel algorithms; Perona-Malik case; algorithm parallelization; anisotropic Beltrami flow; anisotropic alpha-kernel decomposition; anisotropic alpha-kernel flow; computational complexity; graph Laplacian method; image patch; inhomogeneous diffusion process; scale space analysis; Anisotropic magnetoresistance; Eigenvalues and eigenfunctions; Graph theory; Information science; Laplace equations; Mathematics; Power engineering computing; Power generation; Space heating; Symmetric matrices;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    2160-7508
  • Print_ISBN
    978-1-4244-2339-2
  • Electronic_ISBN
    2160-7508
  • Type

    conf

  • DOI
    10.1109/CVPRW.2008.4562982
  • Filename
    4562982