This project focuses on the Markov Random Field modeling for image classification problem. For most 2D images with reasonable resolutions, pixels have spatial constraints, which should be enforced during the classification. For the sake of computational simplicity, the identical independent distributed (I.I.D.) assumption is commonly used. Due to this assumption, some unreasonable holes will ap...

Markov Random Field (MRF) models with potentials learned from the data have recently received attention for learning the low-level structure of natural images. A MRF provides a principled model for whole images, unlike ICA, which can in practice be estimated for small patches only. However, learning the filters in an MRF paradigm has been problematic in the past since it required computationall...

Breast cancer is a major public health problem for women in the Iran and many other parts of the world. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. But segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils is a ...

This article presents a novel algorithm for image segbeen developed for classification purposes. In addition, many mentation via the use of the multiresolution wavelet analysis and the authors have discovered significant advantages in the use of the expectation maximization (EM) algorithm. The development of a multiresolution concept [4,5] . Brazkovic and Neskovic presented multiresolution wave...

High resolution satellite images may contain shadows due to the limitations of imaging circumstances and presence of tall-standing objects. These shadows cause problems in the exploitation of such images. This paper proposes a complete processing chain to mitigate these shadow effects. This processing chain has two parts. A shadow detection part bases on image imposing and a pixel restoration p...

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation ...

This paper addresses the problem of clutter patch identification based on Markov random field (MRF) models. MRF has long been recognized by the image processing community to be an accurate model to describe a variety of image characteristics such as texture. Here, we use the MRF to model clutter patch characteristics, captured by a radar receiver or radar imagery equipment, due to the fact that...

This paper presents a promising super-resolution (SR) approach using maximum a posteriori (MAP) estimation. We consider the high resolution (HR) estimation as a Markov Random Field (MRF), using a transformed gradient field prior to repair the image fuzzy problem caused by MRF. An improved Normalized Convolution method is proposed to obtain a first good estimation. We build a reasonable energy f...

In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum,...

In this paper, we propose a new technique to update a probability vector [1] for Estimation of Distribution Algorithms (EDA)[15]. We present a novel algorithm belonging to the general class of EDA which we call Distribution Estimation using Markov Random Fields (DEUM). In common with other EDAs, DEUM uses a population of chromosomes to build a probabilistic model of good solutions. The model is...