Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
On a Parameter Estimation Method for Gibbs-Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated estimation of the parameters of Gibbs priors to be used in binary tomography
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Automatic speechreading with applications to human-computer interfaces
EURASIP Journal on Applied Signal Processing
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We examine the histogram method proposed in [1] for estimating the parameters associated with a Markov random field. This method relies on the estimation of the local interaction sums from histogram data. We derive an estimator for these quantities that is optimal in a well-defined sense. Furthermore, we show that the final step of the histogram method, the solution of a least-squares problem, can be done substantially faster than one might expect if no equation culling is used. We also examine the use of weighted least-squares and see that this seems to lead to better estimates even with small amounts of data.