International Journal of Computer Vision

, Volume 38, Issue 2, pp 173–186 | Cite as

Contour Tracking in Clutter: A Subset Approach

  • Daniel Freedman
  • Michael S. Brandstein


A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned model guides the algorithm in picking out the correct one. The algorithm, which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented.

contour tracking low-level vision visual clutter subset learning iterative minimization Legendre polynomials morphological filters 


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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Daniel Freedman
    • 1
  • Michael S. Brandstein
    • 1
  1. 1.Division of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

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