Shape Distances for Binary Image Segmentation

  • Frank R. SchmidtEmail author
  • Lena Gorelick
  • Ismail Ben Ayed
  • Yuri Boykov
  • Thomas Brox
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Shape distances are an important measure to guide the task of shape classification. In this chapter we show that the right choice of shape similarity is also important for the task of image segmentation, even at the absence of any shape prior. To this end, we will study three different shape distances and explore how well they can be used in a trust region framework. In particular, we explore which distance can be easily incorporated into trust region optimization and how well these distances work for theoretical and practical examples.


Image Segmentation Trust Region Shape Space Trust Region Method Sign Distance Function 
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 International Publishing Switzerland 2016

Authors and Affiliations

  • Frank R. Schmidt
    • 1
    Email author
  • Lena Gorelick
    • 2
  • Ismail Ben Ayed
    • 3
  • Yuri Boykov
    • 2
  • Thomas Brox
    • 1
  1. 1.Computer Science Department and BIOSS Centre for Biological Signalling StudiesUniversity of FreiburgFreiburgGermany
  2. 2.Computer Science DepartmentUniversity of Western OntarioLondonCanada
  3. 3.École de Technologie SupérieureUniversity of QuebecMontrealCanada

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