• DocumentCode
    905730
  • Title

    Tracking deformable objects in the plane using an active contour model

  • Author

    Leymarie, Frédéric ; Levine, Martin D.

  • Author_Institution
    Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
  • Volume
    15
  • Issue
    6
  • fYear
    1993
  • fDate
    6/1/1993 12:00:00 AM
  • Firstpage
    617
  • Lastpage
    634
  • Abstract
    The problems of segmenting a noisy intensity image and tracking a nonrigid object in the plane are discussed. In evaluating these problems, a technique based on an active contour model commonly called a snake is examined. The technique is applied to cell locomotion and tracking studies. The snake permits both the segmentation and tracking problems to be simultaneously solved in constrained cases. A detailed analysis of the snake model, emphasizing its limitations and shortcomings, is presented, and improvements to the original description of the model are proposed. Problems of convergence of the optimization scheme are considered. In particular, an improved terminating criterion for the optimization scheme that is based on topographic features of the graph of the intensity image is proposed. Hierarchical filtering methods, as well as a continuation method based on a discrete sale-space representation, are discussed. Results for both segmentation and tracking are presented. Possible failures of the method are discussed
  • Keywords
    convergence; filtering and prediction theory; graph theory; image segmentation; optimisation; tracking; active contour model; cell locomotion; continuation method; convergence; deformable objects; discrete sale-space representation; hierarchical filtering; image segmentation; noisy intensity image; nonrigid object; optimization; segmentation; snake; terminating criterion; topographic features; tracking; Active contours; Automation; Cells (biology); Convergence; Deformable models; Filtering; Image segmentation; Shape; Surface topography; Tracking;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.216733
  • Filename
    216733