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
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin detection using pairwise models
Image and Vision Computing
Skin detection for single images using dynamic skin color modeling
Pattern Recognition
Skin and non-skin probability approximation based on discriminative tree distribution
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fast, quality, segmentation of large volumes – isoperimetric distance trees
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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Methods for approximately computing the marginal probability mass functions and means of a Markov random field (MRF) by approximating the lattice by a tree are described. Applied to the a posteriori MRF these methods solve Bayesian spatial pattern classification and image restoration problems. The methods are described, several theoretical results concerning fixed-point problems are proven, and four numerical examples are presented, including comparison with optimal estimators and the Iterated Conditional Mode estimator and including two agricultural optical remote sensing problems.