Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extensions of a theory of networks for approximation and learning: outliers and negative examples
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale minimization of global energy functions in some visual recovery problems
CVGIP: Image Understanding
Regularization theory and neural networks architectures
Neural Computation
International Journal of Computer Vision
Robustly estimating changes in image appearance
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gauss-Markov Measure Field Models for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Rest Condition Potentials: Second Order Edge-Preserving Regularization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
MAP image restoration and segmentation by constrained optimization
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Entropy controlled gauss-markov random measure field models for early vision
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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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.