• DocumentCode
    2465145
  • Title

    Parametrically deformable contour models

  • Author

    Staib, Lawrence H. ; Duncan, James S.

  • Author_Institution
    Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA
  • fYear
    1989
  • fDate
    4-8 Jun 1989
  • Firstpage
    98
  • Lastpage
    103
  • Abstract
    Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based shape information. Flexible constraints, in the form of a probabilistic deformable model, are applied to the problem of segmenting natural objects whose diversity and irregularity of shape makes them poorly represented in terms of fixed features of forms. The parametric model is based on the elliptic Fourier decomposition of the boundary. The segmentation problem is solved as an optimization problem, where the best match between the boundary (as defined by the parameter vector) and the image data is found. Initial experimentation shows good results on a variety of images
  • Keywords
    Fourier analysis; optimisation; pattern recognition; picture processing; elliptic Fourier decomposition; feature extraction; model-based shape information; optimization; parameter vector; parametrically deformable contour model; pattern recognition; picture processing; segmentation; Computed tomography; Deformable models; Heuristic algorithms; Image segmentation; Information analysis; Marine vehicles; Parametric statistics; Radiology; Rubber; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89., IEEE Computer Society Conference on
  • Conference_Location
    San Diego, CA
  • ISSN
    1063-6919
  • Print_ISBN
    0-8186-1952-x
  • Type

    conf

  • DOI
    10.1109/CVPR.1989.37834
  • Filename
    37834