Image segmentation by polygonal Markov Fields

Unsupervised image segmentation using triplet Markov fields

Hidden Markov fields (HMF) models are widely applied to various problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of HMF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, whi...

Markov Random Fields in Image Segmentation

Statistical image segmentation using Triplet Markov fields

Hidden Markov fields (HMF) are widely used in image processing. In such models, the hidden random field of interest S s s X X ∈ = ) ( is a Markov field, and the distribution of the observed random field S s s Y Y ∈ = ) ( (conditional on X ) is given by ∏ ∈ = S s s s x y p x y p ) ( ) ( . The posterior distribution ) ( y x p is then a Markov distribution, which affords different Bayesian process...

Cluster-Based Image Segmentation Using Fuzzy Markov Random Field

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...

Extended Markov Random Fields for predictive image segmentation

Since the 1970s, there has been increasing interest in the use of Markov Random Fields (MRFs) as models to aid in the segmentation of noisy or degraded digital images. MRFs can make up for deficiencies in observed information by adding a-priori knowledge to the image interpretation process in the form of models of spatial interaction between neighbouring pixels. In data fusion problems, interac...