Edge Strength Functions as Shape Priors in Image Segmentation

  • Erkut Erdem
  • Aykut Erdem
  • Sibel Tari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Grimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator function, known as the “edge strength function”, which provides a distance-like surface for the shape boundary. Our method can handle arbitrary deformations due to shape variability as well as plane Euclidean transformations. The method is also robust with respect to noise and missing parts. Furthermore, this formulation does not require simple closed curves as in a typical level set formulation.


Image Segmentation Segmentation Result Active Contour Shape Boundary Simple Closed Curf 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Erkut Erdem
    • 1
  • Aykut Erdem
    • 1
  • Sibel Tari
    • 1
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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