• DocumentCode
    1741624
  • Title

    Vector probability diffusion

  • Author

    Pardo, Alvaro ; Sapiro, Guillermo

  • Author_Institution
    Inst. of Math., Univ. de la Republica, Montevideo, Uruguay
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    884
  • Abstract
    A method for isotropic and anisotropic diffusion of vector probabilities in general, and posterior probabilities in particular, is introduced. The technique is based on diffusing via coupled partial differential equations restricted to the semi-hyperplane corresponding to probability functions. Both the partial differential equations and their corresponding numerical implementation guarantee that the vector remains a probability vector, having all its components positive and adding to one. Applying the method to posterior probabilities in classification problems, spatial and contextual coherence is introduced before the MAP decision, thereby improving the classification results
  • Keywords
    image classification; image segmentation; partial differential equations; probability; vectors; MAP decision; anisotropic diffusion; classification problems; contextual coherence; coupled partial differential equations; image classification; image segmentation; isotropic diffusion; numerical implementation; posterior probabilities; probability functions; semi-hyperplane; spatial coherence; vector probability diffusion; Anisotropic magnetoresistance; Diffusion processes; Engineering profession; Image segmentation; Labeling; Layout; Markov random fields; Mathematics; Partial differential equations; Spatial coherence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2000. Proceedings. 2000 International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1522-4880
  • Print_ISBN
    0-7803-6297-7
  • Type

    conf

  • DOI
    10.1109/ICIP.2000.901101
  • Filename
    901101