Computer Vision — ECCV 2002

Volume 2352 of the series Lecture Notes in Computer Science pp 621-634


Image Segmentation by Flexible Models Based on Robust Regularized Networks

  • Mariano RiveraAffiliated withDepartment of Radiology, University of PennsylvaniaCentro de Investigacion en Matematicas A.C.
  • , James GeeAffiliated withDepartment of Radiology, University of Pennsylvania

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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.


Segmentation Restoration Edge-preserving Regularization