Image Segmentation by Flexible Models Based on Robust Regularized Networks

  • Mariano Rivera
  • James Gee
Conference paper

DOI: 10.1007/3-540-47977-5_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2352)
Cite this paper as:
Rivera M., Gee J. (2002) Image Segmentation by Flexible Models Based on Robust Regularized Networks. In: Heyden A., Sparr G., Nielsen M., Johansen P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2352. Springer, Berlin, Heidelberg

Abstract

The object of this paper is to present a formulation for the segmentation and restoration problem using flexible models with a robust regularized network (RRN). A two-steps iterative algorithm is presented. In the first step an approximation of the classification is computed by using a local minimization algorithm, and in the second step the parameters of the RRN are updated. The use of robust potentials is motivated by (a) classification errors that can result from the use of local minimizer algorithms in the implementation, and (b) the need to adapt the RN using local image gradient information to improve fidelity of the model to the data.

Keywords

Segmentation Restoration Edge-preserving Regularization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Mariano Rivera
    • 1
    • 2
  • James Gee
    • 1
  1. 1.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Centro de Investigacion en Matematicas A.C.GuanajuatoMexico

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