• DocumentCode
    1878256
  • Title

    Object discovery with perceptual grouping

  • Author

    Liu, David ; Chen, Tsuhan

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA
  • fYear
    2008
  • fDate
    12-15 Oct. 2008
  • Firstpage
    3032
  • Lastpage
    3035
  • Abstract
    We propose an iterative method for discovering objects in images. In each iteration, the current estimate of the layout is processed by a sequence of perceptual grouping rules. Perceptual grouping appears to be the basis of visual organization of human. It is concerned with the problem of the formation of wholes from parts. The method does not rely on the mixture of Gaussian model and hence avoids the model selection problem. We use synthetic and real images to demonstrate that the obtained result is better than that obtained by other methods.
  • Keywords
    image processing; iterative methods; human perception; image objects; perceptual grouping rules; posterior map; probabilistic model; spatial distribution; Bayesian methods; Data mining; Gaussian distribution; Humans; Image segmentation; Iterative methods; Layout; Low pass filters; Probability distribution; Unsupervised learning; EM algorithm; low pass filter; unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
  • Conference_Location
    San Diego, CA
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4244-1765-0
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2008.4712434
  • Filename
    4712434