• DocumentCode
    397570
  • Title

    Kernel snakes: non-parametric active contour models

  • Author

    Abd-Almageed, Wael ; Smith, Christopher E. ; Ramadan, Samah

  • Author_Institution
    Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
  • Volume
    1
  • fYear
    2003
  • fDate
    5-8 Oct. 2003
  • Firstpage
    240
  • Abstract
    In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.
  • Keywords
    Bayes methods; decision theory; image segmentation; nonparametric statistics; target tracking; Bayesian decision theory; kernel snakes; multicolored target tracking; nonparametric active contour models; nonparametric generalized formulation; nonparametric model; nonparametric techniques; real time performance; statistical pressure snakes; Active contours; Artificial intelligence; Bayesian methods; Decision theory; Deformable models; Image edge detection; Intelligent robots; Kernel; Laboratories; Robot vision systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2003. IEEE International Conference on
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-7952-7
  • Type

    conf

  • DOI
    10.1109/ICSMC.2003.1243822
  • Filename
    1243822