Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter Estimation in Markov Random Field Contextual Models Using Geometric Models of Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary vectors partially determined by linear equation systems
Discrete Mathematics
On the Estimation of Markov Random Field Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A coordinate ascent approach to tomographic reconstruction of label images from a few projections
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
A coordinate ascent approach to tomographic reconstruction of label images from a few projections
Discrete Applied Mathematics - Special issue: IWCIA 2003 - Ninth international workshop on combinatorial image analysis
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Image modeling using Gibbs priors was previously shown, based on experiments, to be effective in image reconstruction problems. This motivated us to evaluate three methods for estimating the priors. Two of them accurately recover the parameters of the priors; however, all of them are useful for binary tomography. This is demonstrated by two sets of experiments: in one the images are from a Gibbs distribution and in the other they are typical cardiac phantom images.