Showing papers on "Markov random field published in 1997"

Statistical approach to segmentation of single-channel cerebral MR images

Abstract: A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.

Markov random field segmentation of brain MR images

Abstract: Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented.

Wavelet-based image denoising using a Markov random field a priori model

Abstract: This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.

Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data

Abstract: In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values, from observed image data. Many of these, procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. Here, the authors use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. The authors utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. The authors consider two different Markov random field (MRF) models-the power model and a line-site model. As applications they estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom.

Inverse halftoning via MAP estimation

Abstract: There has been a tremendous amount of research in the area of image halftoning, where the goal has been to find the most visually accurate representation given a limited palette of gray levels (often just two, black and white). This paper focuses on the inverse problem, that of finding efficient techniques for reconstructing high-quality continuous-tone images from their halftoned versions. The proposed algorithms are based on a maximum a posteriori (MAP) estimation criteria using a Markov random field (MRF) model for the prior image distribution. Image estimates obtained with the proposed model accurately reconstruct both the smooth regions of the image and the discontinuities along image edges. Algorithms are developed and example gray-level reconstructions are presented generated from both dithered and error-diffused halftone originals. Application of the technique to the problems of rescreening and the processing of halftone images are shown.

Character extraction of license plates from video

Abstract: In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as a Markov random field (MRF). With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted. It is shown that our approach provides better performance than other single frame methods.

Object-based estimation of dense motion fields

Abstract: Motion estimation belongs to key techniques in image sequence processing. Segmentation of the motion fields such that, ideally, each independently moving object uniquely corresponds to one region, is one of the essential elements in object-based image processing. This paper is concerned with unsupervised simultaneous estimation of dense motion fields and their segmentations. It is based on a stochastic model relating image intensities to motion information. Based on the analysis of natural images, a region-based model of motion-compensated prediction error is proposed. In each region the error is modeled by a white stationary generalized Gaussian random process. The motion field and its segmentation are themselves modeled by a compound Gibbs/Markov random field accounting for statistical bindings in spatial direction and along the direction of motion trajectories. The a posteriori distribution of the motion field for a given image sequence is formulated as an objective function, such that its maximization results in the MAP estimate. A deterministic multiscale relaxation technique with regular structure is employed for optimization of the objective function. Simulation results are in a good agreement with human perception for both the motion fields and their segmentations.

Synthetic aperture radar image segmentation by a detail preserving Markov random field approach

Abstract: A multichannel image segmentation method is imposed that utilizes Markov random fields (MRFs) with adaptive neighborhood (AN) systems. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighborhood system is achieved by following a criterion that makes use of the Markovian property exploiting the local image content. The MRF segmentation approach with AN systems (MRF-AN) makes it possible to better preserve small features and border areas. The purpose of the paper is to show the usefulness of the concept of MRF-AN for SAR image segmentation.

Separation of Moving Regions from Background in an Image Sequence Acquired with a Mobil Camera

Abstract: We present a statistical method to detect regions whose apparent motion in the image is not conforming to the dominant motion of the background resulting from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D parametric motion model, and compensated for using the values of the motion model parameters estimated by a multiresolution robust statistical technique. Then, regions whose motion cannot be described by this global model estimated over the entire image, are extracted. The detection of these non conforming regions is achieved through a statistical regularization approach based on multiscale Markov random field (MRF) models. We have paid a particular attention to the definition of the energy function involved and to the observations taken into account. To gain robustness, information is integrated over time. This method has been validated by experiments carried out on many real image sequences.

Unsupervised Image Segmentation Using Markov Random Field Models

Abstract: We present an unsupervised segmentation algorithm based on a Markov Random Field model for noisy images The algorithm finds the the most likely number of classes, their associated model parameters and generates a corresponding segmentation of the image into these classes This is achieved according to the MAP criterion To facilitate this, an MCMC algorithm is formulated to allow the direct sampling of all the above parameters from the posterior distribution of the image To allow the number of classes to be sampled, a reversible jump is incorporated into the Markov Chain The jump enables the possible splitting and combining of classes and consequently, their associated regions within the image Experimental results are presented showing rapid convergence of the algorithm to accurate solutions

Volumetric object reconstruction using the 3D-MRF model-based segmentation [magnetic resonance imaging]

Abstract: A number of segmentation algorithms have been developed, but those algorithms are not effective on volume reconstruction because they are limited to operating only on two-dimensional (2-D) images Here, the authors propose the volumetric object reconstruction method using the three-dimensional Markov random field (3D-MRF) model-based segmentation The 3D-MRF model is known to be one of the most efficient ways to model spatial contextual information The method is compared with the 2-D region growing scheme under three types of interpolation The results show that the proposed method is better in terms of image quality than the other methods

Image segmentation using multiresolution wavelet analysis and expectation‐maximization (EM) algorithm for digital mammography

Abstract: This article presents a novel algorithm for image seg- been 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 wavelet feature extraction scheme is based on the the Gaussian pyramid and fuzzy linking method for the adaptive Gaussian Markov random field (GMRF) assumption in mammo- detection of cancerous changes in mammograms (6). graphic image modeling. Mammographic images are hierarchically Recently, as a result of cross-fertilization of innovative ideas decomposed into different resolutions. In general, larger breast le- from image processing, spatial statistics, and statistical physics, sions are characterized by coarser resolutions, whereas higher resolu- a significant amount of research activity on image modeling and tions show finer and more detailed anatomical structures. These hier- archical variations in the anatomical features displayed by multiresolu- segmentation has also been concentrated on the two-dimensional tion decomposition are further quantified through the application of ( 2D ) Markov random field ( MRF ) . Although many of the poten- the Gaussian Markov random field. Because of its uniqueness in local- tials of MRF had been envisioned by the early works of Levy ity, adaptive features based on the nonstationary assumption of (7), McCormick and Jayaramamrhy (8), and Abend et al. (9), GMRF are defined for each pixel of the mammogram. Fibroadenomas exploitation of the powers of the MRF was not possible until are then segmented via the fuzzy C-means algorithm using these significant recent advances occurred in the appropriate mathemat- localized features. Subsequently, the segmentation results are further ical and computational tools. Chellappa and Kashyap (10 ) suc- enhanced via the introduction of a maximum a posteriori (MAP) seg- cessfully applied the noncausal autoregressive ( NCAR ) model mentation estimation scheme based on the Bayesian learning para-

Markov random field image segmentation using cellular neural network

Abstract: Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands of cells. Herein we use the CNN UM architecture for statistical image segmentation. The Modified Metropolis Dynamics (MMD) method can be implemented into the raw analog architecture of the CNN. We are able to implement a (pseudo) random field generator using one layer (one memory/cell) of the CNN. We can introduce the whole pseudostochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, a real VLSI CNN chip can execute a pseudostochastic relaxation algorithm of about 100 iterations in about 1 ms. In the proposed solution the segmentation is unsupervised. We have developed a pixel-level statistical estimation model. The CNN turns the original image into a smooth one. Then we have two gray-level values for every pixel: the original and the smoothed one. These two values are used for estimating the probability distribution of region label at a given pixel. Using the conventional first-order Markov Random Field (MRF) model, some misclassification errors remained at the region boundaries, because of the estimation difficulties in case of low SNR. By using a greater neighborhood, this problem has been avoided. In our CNN experiments, we used a simulation system with a fixed-point integer precision of 16 bits. Our results show that even in the case of the very constrained conditions of value-representations (the interval is (-64,+64), the accuracy is 0.002) can result in an effective and acceptable segmentation.

Automatic license extraction from moving vehicles

Abstract: We present a new approach to extract the license from an image sequence of moving vehicles. The approach includes the following components: 1) license plate localization; 2) feature extraction and tracking; 3) perspective distortion correction; 4) binarization. We model the binarization of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function based on MRF modeling. In the experiments, we compared our results with other two methods that were evaluated. Our method has demonstrated better performance.

A Markov random field approach to Bayesian speaker adaptation

Abstract: Speaker adaptation through Bayesian learning methodology is studied in this paper. In order to utilize the cross allophone correlations, a Markov random field (MRF) model is proposed as the joint prior distribution of the mean vectors of the allophones. Neighborhoods are defined as pairs of parameters between which strong correlations have been observed previously. Maximum a posteriori estimates of the mean vectors are obtained through an iterative optimization technique that converges to the global maximum of the posterior distribution. This process is similar to a recursive prediction of the parameters, where at each iteration each parameter is estimated by a weighted sum of two terms, the first predicted by the neighbors and the second by the samples. Further Bayesian smoothing of the output distributions is carried out by utilizing some simplifications on the functional forms of the marginal posterior distributions. The proposed method is fast, consuming only a few CPU minutes for processing hundreds of sentences from a new speaker on an IBM RS6000 Model 580 system. Experimental results show rapid improvement of recognition accuracy.

Coding artifact removal with multiscale postprocessing

Abstract: A multiscale post processing algorithm based on constrained optimization with the Huber Markov random field (HMRF) model is investigated. The decoded image is enhanced from coarse to fine scales, where postprocessing at the coarse scale improves the global appearance of the image and reduces long range artifacts such as ringing while postprocessing at the fine scale keeps the sharpness of edges. The efficiency of the proposed algorithm is supported by experimental results.

Unsupervised Markovian segmentation of sonar images

Abstract: This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the an iterative method called iterative conditional estimation (ICE). This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label field (modeled by a Markov random field (MRF)). For the estimation step we use a maximum likelihood estimation for the noise model parameters and the least square method proposed by Derin et al. (1987) to estimate the MRF prior model. Then, in order to obtain a good segmentation and to speed up the convergence rate, we use a multigrid strategy with the previously estimated parameters. This technique has been successfully applied to real sonar images and is compatible with an automatic treatment of massive amounts of data.

A fast suboptimal approach to error concealment in encoded video streams

Abstract: In ATM networks cell loss or channel errors can cause data to be dropped in the channel. When digital video is transmitted over these networks one must be able to reconstruct the missing data so that the impact of these errors is minimized. In this paper we describe a Bayesian approach to concealing these errors by post-processing the received data. In a previous paper (see IEEE Proc. Int. Conf. on Image Processing p.49-52, 1996), each frame in the sequence was modeled as a Markov random field, and maximum a posteriori estimates of the missing macroblocks were obtained. However, the maximum a posteriori estimate is not unique, and the algorithm is also computationally intensive. In this paper we demonstrate, that by using median filtering we arrive at a suboptimal estimate. This will allow real-time nearly optimal reconstruction of the missing data.

Fully Bayesian image segmentation-an engineering perspective

Abstract: Developments in Markov chain Monte Carlo procedures have made it possible to perform fully Bayesian image segmentation. By this we mean that all the parameters are treated identically, be they the segmentation labels, the class parameters or the Markov random field prior parameters. We perform the analysis by sampling from the posterior distribution of all the parameters. Sampling from the MRF parameters has traditionally been considered if not intractable then at least computationally prohibitive. In the statistics literature there are descriptions of experiments showing that the MRF parameters may be sampled by approximating the partition function. These experiments are all, however, on 'toy' problems; for the typical size of image encountered in engineering applications the phase transition behaviour of the models becomes a major limiting factor in the estimation of the partition function. Nevertheless, we show that, with some care, fully Bayesian segmentation can be performed on realistic sized images. We also compare the fully Bayesian approach with the approximate pseudolikelihood method.

Minimization of MRF Energy with Relaxation Labeling

Abstract: Recently, there has been increasing interest in Markov random field (MRF) modeling for solving a variety of computer vision problems formulated in terms of the maximum a posteriori (MAP) probability. When the label set is discrete, such as in image segmentation and matching, the minimization is combinatorial. The objective of this paper is twofold: Firstly, we propose to use the continuous relaxation labeling (RL) as an alternative approach for the minimization. The motivation is that it provides a good compromise between the solution quality and the computational cost. We show how the original combinatorial optimization can be converted into a form suitable for continuous RL. Secondly, we compare various minimization algorithms, namely, the RL algorithms proposed by Rosenfeld et al., and by Hummel and Zucker, the mean field annealing of Peterson and Soderberg, simulated annealing of Kirkpatrick, the iterative conditional modes (ICM) of Besag and an annealing version of ICM proposed in this paper. The comparisons are in terms of the minimized energy value (i.e., the solution quality), the required number of iterations (i.e., the computational cost), and also the dependence of each algorithm on heuristics.

Space-Time Statistical Model for Functional MRI Image Sequences

Abstract: Changes in cerebral blood oxygenation and flow during activation of human brain can be measured using functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation. Ideally, spatial and temporal correlations in the acquired data should all be taken into account to derive statistical parametric maps (SPM) and to identify significant changes in fMRI signal. This paper proposes a multivariate statistical model for brain activation detection accounting for both the spatial and temporal correlations. This model considers a space-time variant error and a spatial Markov random field process is used to yield an unbiased estimate of the SPM. As the number of pixels is large enough, the asymptotic theory is used to derive a threshold allowing the identification of activated areas in the SPM. The method is illustrated on sensorimotor experiments performed on normal subjects using 1.5T gradient-echo MRI.

Using Markov random fields for contour-based grouping

Abstract: To overcome fragmentation of an initial contour-based segmentation and to organize contour segments into image primitives on a higher level of abstraction, regularities of the image data are exploited using ideas from the Gestalt psychology. First, groups are hypothesized within a hierarchy based on local evidence only, where the criteria are derived from a hand labelled training set. These hypotheses are subsequently judged in a global context using a Markov random field to derive a global interpretation. Examples of results for real data are given.

Maximum Likelihood Estimation of Markov Random Field Parameters Using Markov Chain Monte Carlo Algorithms

Abstract: Recent developments in statistics now allow maximum likelihood estimators for the parameters of Markov Random Fields to be constructed. We detail the theory required, and present an algorithm which is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models, the standard Potts model, an inhomogeneous variation of the Potts model and a long-range interaction model, better adapted to modelling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesise the models to demonstrate which features of the image have been captured by the model.

Perceptual Grouping of Contour Segments Using Markov Random Fields

Abstract: The aim of this work is to exploit regular structure in a scene by using the gestalt laws of perception in the field of computer vision. The statistical result of a hand labelled training set is employed to derive ‘‘Areas of perceptual attentiveness’’. Grouping hypotheses are thus generated based on local evidence. To judge these hypotheses in a more global context a Markov random field is used. The approach is contour-based and different types of grouping hypotheses define a hierarchy according to their growing complexity.

Lip motion automatic detection

Abstract: An algorithm for speaker's lip motion detection is presented, based on the processing of a colour video sequence of speaker's face under natural lighting conditions and without any particular make-up. It is intended for applications in speech recognition, videoconferencing or speaker's face synthesis and animation. The algorithm is based on a statistical approach using Markov Random Field (MRF) modelling, with a spatiotemporal neighbourhood of the pixels in the image sequence. Two kinds of observations are used : the temporal difference between successive images (motion information) and the purity of red hue in the current and past images (spatial information about lip location). The field of hidden labels, relevant for lip motion detection, is obtained by energy minimisation and proves to be robust to lighting conditions (shadows). This label field is used to extract qualitative information (mouth opening and closing) but also quantitative information by measuring some geometrical features (horizontal and vertical lip spacing) directly on the label field.

Unsupervised image segmentation using a telegraph parameterization of Pickard random fields

Abstract: This article presents a non-supervised segmentation method based upon a discrete-level unilateral Markov field model of the image. Such models have been shown to yield numerically efficient algorithms, for segmentation and for hyperparameter estimation as well. Our contribution lies in the derivation of a parsimonious telegraphic parameterization of the unilateral Markov field. On a theoretical level, this parameterization ensures that some important properties of the field (e.g., stationarity) do hold. On a practical level, it reduces the computational complexity of the algorithm used in the segmentation and parameter estimation stages of the procedure. In addition, it decreases the number of hyperparameters that must be estimated, thereby improving the convergence speed and accuracy of the corresponding estimation method.

Recognition of Facial Images with Low Resolution Using a Hopfield Memory Model

Abstract: In this paper, a new method for recognizing the facial images with low resolution by utilizing the Hopfield model is presented.

Unsupervised Segmentation Applied on Sonar Images

Abstract: This work deals with unsupervised sonar image segmentation. We present a new estimation segmentation procedure using the recent iterative method of estimation called Iterative Conditional Estimation (ICE) [1]. This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label field (modeled by a Markov Random Field (MRF)). For the estimation step, we use a maximum likelihood technique to estimate the noise model parameters, and the least squares method proposed by Derin et al. [2] to estimate the MRF prior model. Then, in order to obtain an accurate segmentation map and to speed up the convergence rate, we use a multigrid strategy exploiting the previously estimated parameters. This technique has been successfully applied to real sonar images 1, and is compatible with an automatic processing of massive amounts of data.