A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of textured images using Gibbs random fields
Computer Vision, Graphics, and Image Processing
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture modeling using Gibbs distributions
CVGIP: Graphical Models and Image Processing
Maximum likelihood unsupervised textured image segmentation
CVGIP: Graphical Models and Image Processing
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
On the Estimation of Markov Random Field Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theory and Use of the EM Algorithm
Foundations and Trends in Signal Processing
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This correspondence is about a Gibbs-Markov random field (GMRF) parameter estimation technique proposed by Derin and Elliott (1987). We will refer to this technique as the histogramming (H) method. First, the relation of the H method to the (conditional) maximum likelihood method is considered. Second, a bias-reduction based modification of the H method is proposed to improve its performance, especially in the case of small amounts of image data.