Local Statistic Based Region Segmentation with Automatic Scale Selection

  • Jérome Piovano
  • Théodore Papadopoulo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)


Recently, new segmentation models based on local information have emerged. They combine local statistics of the regions along the contour (inside and outside) to drive the segmentation procedure. Since they are based on local decisions, these models are more robust to local variations of the regions of interest (contrast, noise, blur, ...). They nonetheless also introduce some new difficulties which are inherent to the fact of basing a global property (the segmentation) on pure local decisions. This papers explores some of those difficulties and proposes some possible corrections. Results on both 2D and 3D data are compared to those obtained without these corrections.


Image Segmentation Active Contour Image Edge Active Contour Model Homogeneous Area 
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 2008

Authors and Affiliations

  • Jérome Piovano
    • 1
  • Théodore Papadopoulo
    • 1
  1. 1.Odyssée Project Team, INRIA Sophia Antipolis - MéditerranéeFrance

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