Unsupervised Segmentation of Synthetic Aperture Radar Sea Ice Imagery Using MRF Models
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative Model of Terrain for Autonomous Navigation in Vegetation
International Journal of Robotics Research
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Computers in Biology and Medicine
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
| Hi-index | 35.68 |
Markov random fields are used extensively in model-based approaches to image segmentation and, under the Bayesian paradigm, are implemented through Markov chain Monte Carlo (MCMC) methods. We describe a class of such models (the double Markov random field) for images composed of several textures, which we consider to be the natural hierarchical model for such a task. We show how several of the Bayesian approaches in the literature can be viewed as modifications of this model, made in order to make MCMC implementation possible. From a simulation study, conclusions are made concerning the performance of these modified models