Gaussian Markov Random Field Model Research Articles

FreeCast: Graceful Free-Viewpoint Video Delivery

Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast . FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0–25 dB at a transmission symbol rate of 37.5 Msymbols/s.

A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data

Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.

Modeling of a three-dimensional dynamic thermal field under grid-based sensor networks in grain storage

Thermal management is a major task in granaries, due to the essential role of temperature in grain storage. The accurate acquisition and updating of thermal field information generates a meaningful index for grain quality surveillance and storage maintenance actions. However, given the unknown mechanisms of local uncertainties, including local grain degradation and fungal infections that may significantly vary the thermal field in granaries, the appropriate modeling of field dynamics remains a challenging task. To address this issue, this article combines a three-dimensional (3D) nonlinear dynamics model with a stochastic spatiotemporal model to capture a 3D dynamic thermal map. To best harness the temperature data from the grid-based sensor network, we integrate the Kriging model into the Gaussian Markov random field model by introducing an anisotropic covariance function. Both simulation and real case studies are conducted to validate our proposed approach, and the results show that our approach outperforms other alternative methods for field estimation.

Investigation of a novel automatic micro image-based method for the recognition of animal fibers based on Wavelet and Markov Random Field

In the analysis of fiber recognition, the challenge lies in the texture feature extraction. The main aim of this paper is to present a novel texture feature analysis method based on wavelet multi-scale analysis to fully extract texture features of microscopic images resulting in better recognition of similar animal fibers. Thousands of three kinds of similar fiber images including cashmere, sheep wool and goat hair were captured by the optical microscope and the digital camera. They were pre-processed to obtain the enhanced images with background removed. Then the pretreated fiber images were decomposed by 3-layer wavelet transform, four sub-images under the third-layer wavelet decomposition scale were analyzed by Gauss Markov Random Field (GMRF) model and their model parameters were obtained. Through the difference analysis of different kinds of fibers, two model parameters were selected from each sub-image to generate an 8-dimensional feature vectors, which was used to describe the fiber images. The parameters, which were extracted from 1000 images of each kind of fiber, were copied three times and randomly arranged to generate the final data sets. Finally, the data sets were processed by 10-times cross validation method as the training set and testing set of support vector machine (SVM). Ten different recognition rates could be obtained through the experiment, and the mean value was used as the final recognition accuracy of wool and cashmere fibers. The experimental results indicated that the method had a great recognition rate with 90.07% and the performance was robust. It verifies that the method based on wavelet multi-scale analysis is effective for the recognition of similar fibers.

Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients

ABSTRACTThe Markov random field (MRF) model is a widely used method for remote-sensing image segmentation, especially the object-based MRF (OMRF) method has attracted great attention in recent years. However, the OMRF method usually fails to capture the correlation between regional features by just considering the mixed-Gaussian model. In order to solve this problem and improve the segmentation accuracy, this article proposes a new method, object-based Gaussian-Markov random field model with region coefficients (OGMRF-RC), for remote-sensing image segmentation. First, to describe the complicated interactions among regional features, the OGMRF-RC method employs the region size and edge information as region coefficients to build the object-based linear regression equation (OLRE) for each region. Second, the classic Gaussian-Markov model is extended to region level for modelling the errors in OLREs. Finally, the segmentation is achieved through a principled probabilistic inference designed for the OGMRF-RC method. Experimental results over synthetic texture images and remote-sensing images from different datasets show that the proposed OGMRF-RC method can achieve more accurate segmentation than other state-of-the-art MRF-based methods and the method using convolutional neural networks.

Reconstruction of Radiative Properties Fields in Participating Media by Using the Sequential Quadratic Programing Combined With Regularization Technique

The refractive index and absorption coefficient fields in participating media are reconstructed simultaneously in this work. In the direct model, the coupled radiation–conduction heat transfer in participating media exposed to a pulse laser irradiation is solved by finite volume method (FVM). In the inverse model, the sequential quadratic programming (SQP) algorithm combined with the generalized Gaussian Markov random field (GGMRF) model is employed to solve the reconstruction problem. It is found that the refractive index and absorption coefficient fields cannot be reconstructed simultaneously. A secondary reconstruction technique based on different regularization parameters is proposed to reconstruct the refractive index and absorption coefficient fields simultaneously. All the retrieval results indicate that the proposed secondary reconstruction technique performs accurately and effectively.

Mangrove Species Discrimination from Very High Resolution Imagery Using Gaussian Markov Random Field Model

Mangrove forests are the most productive ecosystem for tropical and subtropical flora, but they are experiencing serious loss. Urgent measures should be taken for conservation and restoration, including investigation of mangrove species constitution using satellite imagery in which texture provides effective information. However, differentiation by texture for various species of mangroves is underexplored. The Gray Level Co-occurrence Matrix (GLCM) has proved to be a common approach to describing texture in most previous studies, while other textural measurements such as the Gaussian Markov Random Field (GMRF) have seldom been tested and applied. This study aimed to provide a comprehensive assessment and comparison of textures using GLCM and GMRF to offer a better understanding of their roles in mangrove species discrimination using very high-resolution satellite data. The experiments were designed to highlight two aspects of textural features - local pattern and textured region size - to assess their effect on mangrove species discrimination based on texture definition. The results indicated that adjusting the textured region size can easily improve texture extraction, and GMRF outperformed GLCM in texture representation of mangrove forest using the same parameters, with an increase in overall accuracy (1.54%, 6.47% and 10.66% on average at three different local pattern sizes).

Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field

In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in $O(n)$-time, where $n$ is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.

Deep Learning Role in Early Diagnosis of Prostate Cancer.

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Application of hybrid SPSO-SQP algorithm for simultaneous estimation of space-dependent absorption coefficient and scattering coefficient fields in participating media

A hybrid stochastic particle swarm optimization (SPSO) and sequential quadratic programming (SQP) algorithm is proposed to estimate the space-dependent absorption coefficient (κa) and scattering coefficient (κs) fields simultaneously. The retrieval results of SQP algorithm are highly affected by the initial guess, and thus, SPSO is adopted to obtain the initial value of SQP because of its global search capability and lesser dependence on initial guess. The regularization term based on the generalized Gaussian Markov random field model is employed to overcome the ill-posed characteristic of the inverse problem. The inverse coupled radiation-conduction problem is solved in a one-dimensional participating medium with uniform refractive and graded refractive indices exposed to a pulse laser, respectively. All the results show that the separate space-dependent κa and κs fields can be estimated simultaneously and accurately from the coupled optical and thermal signals.

Bipartite Approximation for Graph Wavelet Signal Decomposition

To compactly represent a graph signal in the frequency domain, critically sampled biorthogonal wavelet filterbanks have been proposed to decompose signals on bipartite graphs. However, in practice graph signals often reside on general graph structures that are not bipartite. Thus, an original nonbipartite graph must be decomposed into a sequence of bipartite graph approximations, so that the filterbanks can be applied successively for signal decomposition. In this paper, unlike previous proposals that are heuristic in nature, we design new bipartite approximation strategies for model-based and empirically derived probability distributions. In the first case, a signal prior assumes a Gaussian Markov Random Field (GMRF) model parametrized by the original graph. In the second case, beyond the GMRF signal prior, empirical signal observations are available to compute a posterior probability distribution. In both cases, we optimize for energy compaction in the bipartite subgraphs with two criteria: the Kullback–Leibler divergence metric, which encourages preserving the spectral characteristics of the original graph; and multiplicity of eigenvalue at frequency 1 for graph Laplacian, which is the frequency with minimal energy discrimination. The relative importance between the two criteria is determined by a proposed measure that evaluates the degree of mismatch between the signal prior and posterior. Given these criteria, we first design a global numerical optimization, and then propose a local heuristic approach for fast implementation with simplifications leading to local metric computation. Experimental results show that our proposed bipartite subgraph decomposition outperforms competing proposals in terms of energy compaction.

Automated framework for accurate segmentation of pressure ulcer images

Ulcer tissue segmentation is of immense importance in helping medical personnel to assess wounds. This paper introduces a new computational framework employing state-of-the-art image processing techniques to segment pressure ulcer tissue structures from color images. The framework integrates a visual appearance model of an observed input image with prior color information from an available database of previously stored color RGB images. The following four processing steps are performed. First, to minimize the execution time and enhance the segmentation accuracy, a region-of-interest (ROI) of the whole ulcer area is automatically identified based on contrast changes. This step exploits synthetic frequencies of pixelwise intensities, which are calculated by using an electric field energy model to describe relations between the pixelwise intensities. Secondly, visual appearance of the observed image is modeled by a linear combination of discrete Gaussians (LCDG) model in order to estimate marginal probability distributions of three main tissue classes for the grayscale ROI image. Next, the pixel-wise probabilities of these classes for the color ROI image are calculated using the available prior information about the RGB colors on manually segmented database images. Initial labeling is obtained based on both the observed and prior probabilities of pixelwise colors. Finally, to preserve continuity, the labels are refined and normalized using the generalized Gauss-Markov random field (GGMRF) model. Experimental validation on 24 clinical images of pressure ulcers, provided by the Centre IGURCO, showed the high segmentation accuracy of 90.4%.

The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method

ABSTRACTSpeckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy.

Graph Learning From Data Under Laplacian and Structural Constraints

Graphs are fundamental mathematical structures used in various fields to represent data, signals, and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations, and (iii) associated algorithms. Specifically, graph learning problems are posed as the estimation of graph Laplacian matrices from some observed data under given structural constraints (e.g., graph connectivity and sparsity level). From a probabilistic perspective, the problems of interest correspond to maximum a posteriori parameter estimation of Gaussian–Markov random field models, whose precision (inverse covariance) is a graph Laplacian matrix. For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints. The experimental results demonstrate that the proposed algorithms outperform the current state-of-the-art methods in terms of accuracy and computational efficiency.

Automatic blood vessels segmentation based on different retinal maps from OCTA scans

The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1st-order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets (DSC=95.04±3.75%, VVD=8.51±1.49%, and AUC=95.20±1.52%) show the promise of the proposed segmentation approach.

Gaussian Markov Random Field Model without Boundary Conditions

In this study, we analyzed a Gaussian Markov random field model without periodic boundary conditions. On the basis of a Bayesian inference framework, we showed that image restoration, hyperparameter estimation, and an expectation value of free energy can be conducted analytically. Through numerical simulations, we showed the difference between methods with and without periodic boundary conditions and verified the effectiveness of the proposed method.

Application of SQP algorithm for fluorescence tomography with the time-domain equation of radiative transfer

A reconstruction scheme for the fluorescence tomography is investigated based on the time-domain radiative transfer equation (TD-RTE). Two coupled TD-RTEs, which can provide considerable measurement data, are used as the forward model and solved by the discrete ordinate method. The sequential quadratic programming (SQP) is employed to build the reconstruction scheme for solving the inverse problem. The gradient of objective function is calculated efficiently by the adjoint equation technique. Considering the ill-posed nature of the inverse problem, the regularization term based on the generalized Gaussian Markov random field (GGMRF) model is adopted to enhance the reconstructed image. Influence of the initial guess, contrast, noisy data, and shape of the fluorescent target are analyzed. Simulated results show that the proposed algorithm performs efficiently and accurately on reconstructing the distribution of the fluorescence yield.

Spatiotemporal Modeling of Node Temperatures in Supercomputers

ABSTRACTLos Alamos National Laboratory is home to many large supercomputing clusters. These clusters require an enormous amount of power (∼500–2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently, a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this and other machine rooms. This article focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. This same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well. Supplementary materials for this article are available online.

Binary LDPC Codes Decoding Algorithm Based on MRF and FPGA Implementation

The improved LDPC code decoding algorithm mainly refereed to improving decoding performance or reducing the decoding computation complexity. No matter hard decision or soft decision LDPC code decoding algorithm, we can get all ring number by one test, instead of testing each long ring number, after optimizing ring detection algorithm. We putted forward the application of Gaussian Markov Random Field model to realize the source parameter estimation, and make logarithmic likelihood ratio correction of bit sequence received by the channel decoding end. Joining source residual redundancy information to increase the decoder error correction ability. Source estimation adaptive variable can correct coefficient, and it was regulated by error rate. Under the condition that computational complexity increasedlittlely, the LDPC code decoding algorithm based on MRF effectively improved the decoding performance and implemented the improvement of LDPC code decoding algorithm .In the end, we realized the decoding algorithm by using FPGA.

Accurate reconstruction of the optical parameter distribution in participating medium based on the frequency-domain radiative transfer equation**Project supported by the National Natural Science Foundation of China (Grant No. 51476043), the Major National Scientific Instruments and Equipment Development Special Foundation of China (Grant No. 51327803), and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant

Reconstructing the distribution of optical parameters in the participating medium based on the frequency-domain radiative transfer equation (FD-RTE) to probe the internal structure of the medium is investigated in the present work. The forward model of FD-RTE is solved via the finite volume method (FVM). The regularization term formatted by the generalized Gaussian Markov random field model is used in the objective function to overcome the ill-posed nature of the inverse problem. The multi-start conjugate gradient (MCG) method is employed to search the minimum of the objective function and increase the efficiency of convergence. A modified adjoint differentiation technique using the collimated radiative intensity is developed to calculate the gradient of the objective function with respect to the optical parameters. All simulation results show that the proposed reconstruction algorithm based on FD-RTE can obtain the accurate distributions of absorption and scattering coefficients. The reconstructed images of the scattering coefficient have less errors than those of the absorption coefficient, which indicates the former are more suitable to probing the inner structure.