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A Robust Image Segmentation Model Based on Integrated Square Estimation

  • Shuisheng Xie
  • Jundong Liu
  • Darlene Berryman
  • Edward List
  • Charles Smith
  • Hima Chebrolu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

This paper presents a robust segmentation method based on the integrated squared error or L 2 estimation (L 2 E). Formulated under the Finite Gaussian Mixture (FGM) framework, the new model (FGML2E) has a strong discriminative ability in capturing the major parts of intensity distribution without being affected by outlier structures or heavy noise. Comparisons are made with two popular solutions, the EM and FCM algorithms, and the experimental results clearly show the improvement made by our model.

Keywords

Gaussian Mixture Model Segmentation Result Outlier Portion Outlier Structure Popular Solution 
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 2007

Authors and Affiliations

  • Shuisheng Xie
    • 1
  • Jundong Liu
    • 1
  • Darlene Berryman
    • 2
  • Edward List
    • 2
  • Charles Smith
    • 3
  • Hima Chebrolu
    • 3
  1. 1.School of Elec. Eng. & Comp. Sci. 
  2. 2.School of Human & Consumer Sci., Ohio University, Athens OH 
  3. 3.Department of Neurology, University of Kentucky, Lexington KY 

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