Image Segmentation by Flexible Models Based on Robust Regularized Networks

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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.