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Automatic Prior Shape Selection for Image Segmentation

Conference paper
Part of the Association for Women in Mathematics Series book series (AWMS, volume 1)

Abstract

Segmenting images with occluded and missing intensity information is still a difficult task. Intensity based segmentation approaches often lead to wrong results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic prior shape selection and segmentation in a variational scheme.

Keywords

Image Segmentation Prior Shape Sparse Optimization Data Fidelity Term Base Segmentation Model 
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.

Notes

Acknowledgements

The authors would like to thank Luminita Vese from the Department of Mathematics at the University of California, Los Angeles for insightful discussions. The joint research is partially funded via US NIH 1R21EB016535-01 to W.G. and TUBITAK 112E208 to S.T.

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Copyright information

© Springer International Publishing Switzerland & The Association for Women in Mathematics 2015

Authors and Affiliations

  1. 1.Department of MathematicsCase Western Reserve UniversityClevelandUSA
  2. 2.Department of MathematicsUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of Computer EngineeringMiddle East Technical UniversityÇankayaTurkey

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