A method for approximating the density of maximum-likelihood and maximum a posteriori estimates under a Gaussian noise model.
Craig K. AbbeyEric ClarksonHarrison H. BarrettStefan P. MüllerFrank J. RybickiPublished in: Medical Image Anal. (1998)
Keyphrases
- maximum likelihood
- gaussian distribution
- maximum a posteriori
- noise model
- em algorithm
- map estimation
- pairwise
- error rate
- generalized gaussian
- hyperparameters
- bayesian framework
- image reconstruction
- energy function
- markov random field
- cost function
- similarity measure
- gaussian noise
- likelihood function
- computational complexity
- optimization algorithm
- segmentation algorithm
- gaussian mixture model
- optimization method
- expectation maximization
- edge detection
- probabilistic model
- multiresolution