We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a Hierarchical Markov Random Field (MRF) Model. The algorithm consists of a sampling framework which unifies the processes of model selection, parameter estimation and image segmentation, in a single Markov Chain. To achieve this, Reversible Jumps are incorpo...

The traditional active contour models are sensitive to the speckle noise in synthetic aperture radar (SAR) images. In this paper, Markov random field (MRF) theory is incorporated into fuzzy model detect changes of multitemporal SAR proposed method, neighboring information considered modify pointwise prior probability for exploiting mutual and spatial information. addition, we incorporate MRF ge...

Object detection in images is a very active research topic in many disciplines. Probabilistic methods have been applied to the problem with varying degrees of success. A logistic classifier, Markov random field (MRF), and discriminative Random Field (DRF) were used for the detection of man-made structures in natural images. It was found that the MRF and DRF models were often improvements over t...

This paper presents an extension to our work on estimating the probability distribution by using a Markov Random Field (MRF) model in an Estimation of Distribution Algorithm (EDA) [1]. We propose a method that directly samples a MRF model to generate new population. We also present a new EDA, called the Distribution Estimation Using MRF with direct sampling (DEUMd), that uses this method, and i...

Markov random field MRF is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, ...

In this paper the maximum a posteriori (MAP) image reconstruction of magnetoencephalograms (MEG) is investigated. A mathematical framework for vector Markov random field models (MRF) suitable for MEG modeling of brain neuron current dipole activity is developed. A new method for simulating an MRF over a non-uniformly spaced sample grid while approximating an arbitrary desired covariance structu...

The Markov random field (MRF) theory has been accepted as a highly effective framework for designing noise-tolerant nanometer digital VLSI circuits. In MRF-based design, proper feedback lines are used to control noise and keep the circuits in their valid states. However, this methodology has encountered two major problems that have limited the application of highly noise immune MRF-based circui...

UNet [27] is widely used in semantic segmentation due to its simplicity and effectiveness. However, manually-designed architecture applied a large number of problem settings, either with no optimizations, or manual tuning, which time consuming can be sub-optimal. In this work, firstly, we propose Markov Random Field Neural Architecture Search (MRF-NAS) that extends improves the recent Adaptive ...

The Markov Random Field (MRF) theory provides a consistent way for modeling context dependent entities such as image pixels. Trying to solve the image restoration problem in the MRF framework is an optimization problem that is NP hard, and approximation techniques like the belief propagation methods are proposed. The problem of the belief propagation is its inefficiency. In this project, I impl...

This paper describes on-line recognition of handwritten Lao characters by adopting Markov random field (MRF). The character set to recognize includes consonants, vowels and tone marks, 52 characters in total. It extracts feature points along the pen-tip trace from pen-down to pen-up, and then sets each feature point from an input pattern as a site and each state from a character class as a labe...