IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Journal of Signal Processing Systems
Unsupervised data classification using pairwise Markov chains with automatic copulas selection
Computational Statistics & Data Analysis
| Hi-index | 35.68 |
We compare the expectation maximization (EM) algorithm with another iterative approach, namely, the iterative conditional estimation (ICE) algorithm, which was formally introduced in the field of statistical segmentation of images. We show that in case the probability density function (PDF) belongs to the exponential family, the EM algorithm is one particular case of the ICE algorithm