• DocumentCode
    1619808
  • Title

    Motion estimation and segmentation method based on integration of spatial and temporal probability models

  • Author

    Linghu, Yong-Fang

  • Author_Institution
    Guizhou Univ. of Finance & Econ., Guiyang, China
  • fYear
    2009
  • Firstpage
    484
  • Lastpage
    488
  • Abstract
    A novel video motion object automatic segmentation algorithm based on a Bayesian framework is studied in this paper. A fast estimation procedure for the posterior marginals is added to the MAP algorithm.The field is initialized as the temporal segmentation result and the spatial segmentation is provided as an observed field of the image. Firstly, initial segmentation is applied to obtain number of the initial motions and the corresponding initial parameters of the motion model.Then the parameters are updated by using the given parameter estimation method. The experiment results show that the algorithm proposed is effective.
  • Keywords
    Bayes methods; image segmentation; maximum likelihood estimation; motion estimation; probability; spatiotemporal phenomena; video signal processing; Bayesian framework; MAP algorithm; motion estimation; motion segmentation; posterior marginals estimation procedure; spatial probability models; spatial segmentation; temporal probability models; temporal segmentation; video motion object automatic segmentation algorithm; Bayesian methods; Image motion analysis; Image segmentation; Motion estimation; Object segmentation; Optical sensors; Parameter estimation; Partitioning algorithms; Video compression; Videoconference; Bayesian framework; MAP algorithm; Maximizer of the posterior marginals; Spatio-temporal segmentation; video object;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Anti-counterfeiting, Security, and Identification in Communication, 2009. ASID 2009. 3rd International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-3883-9
  • Electronic_ISBN
    978-1-4244-3884-6
  • Type

    conf

  • DOI
    10.1109/ICASID.2009.5276983
  • Filename
    5276983