Image Estimation Using Doubly Stochastic Gaussian Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Learning the parameters of a hidden Markov random field image model: A simple example
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical continuation methods: an introduction
Numerical continuation methods: an introduction
Nonlinear analog networks for image smoothing and segmentation
Journal of VLSI Signal Processing Systems - Parallel processing on VLSI arrays
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Tree Approximations to Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Labeling and Grouping by Minimizing Linear Functionals over Cones
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We describe a family of approximations, denoted by 驴cluster approximations,驴 for the computation of the mean of a Markov random field (MRF). This is a key computation in image processing when applied to the a posteriori MRF. The approximation is to account exactly for only spatially local interactions. Application of the approximation requires the solution of a nonlinear multivariable fixed-point equation for which we prove several existence, uniqueness, and convergence-of-algorithm results. Four numerical examples are presented, including comparison with Monte Carlo calculations.