Markov Random Field Model Research Articles

Superpixel-based change detection for GPR time-lapse slices using fuzzy c-means and the Markov random field method

Ageing and complex underground utility infrastructure present a significant challenge for modern society, requiring long-term monitoring and maintenance to prevent economic and social costs associated with infrastructure degradation and failure. In this study, we proposed an unsupervised superpixel-based change-detection method using ground-penetrating radar time-lapse slices combining fuzzy c-means and the Markov random field model to investigate an invisible subsurface change due to buried void using time-series measurements. First, simple linear iterative clustering was applied to the difference image, which was generated using paired time-lapse images after intensity registration to create different scales superpixel maps. Then, fuzzy c-means clustering was used to generate superpixel-based change maps. Finally, the Markov random field model was used to integrate the information of adjacent neighbourhoods in three dimensions to iteratively refine the change map. We designed two underground cavities (one representing shallow local voids and the other representing voids near pipeline networks) to verify the capability and adaptability of the proposed method. The experimental results demonstrate the feasibility of the method, with F1-scores of 0.82, 0.69, 0.69, and 0.65 and kappa coefficients of 0.81, 0.69, 0.68, and 0.64. Our method represents a significant contribution to the field of GPR-based change detection and has the potential to improve the long-term monitoring and maintenance of complex underground utility infrastructure.

Unsupervised Classification of Non-Gaussian Terrain Based on Polarimetric Decomposition

Polarimetric synthetic aperture radar (PolSAR) can provide complete polarization property of terrain. Terrain classification is the most common application of PolSAR data. In this paper, an unsupervised clustering algorithm based on Freeman-Durden decomposition (FDD) and a non-Gaussian K-Wishart distribution classifier is proposed. This algorithm combines an advanced statistical distribution with spatial polarization scattering information of multi-looks PolSAR data. We use the prior probability characteristics of the Markov random field model to adaptively adjust the cluster center to make the classification more accurate. The experiment result shows that the proposed algorithm based on non-Gaussian models can better retain the polarization information of the target and the clustering accuracy was effectively improved on the real SAR images.

3D City Reconstruction: A Novel Method for Semantic Segmentation and Building Monomer Construction Using Oblique Photography

Existing 3D city reconstruction via oblique photography can only produce surface models, lacking semantic information about the urban environment and the ability to incorporate all individual buildings. Here, we propose a method for the semantic segmentation of 3D model data from oblique photography and for building monomer construction and implementation. Mesh data were converted into and mapped as point sets clustered to form superpoint sets via rough geometric segmentation, facilitating subsequent feature extractions. In the local neighborhood computation of semantic segmentation, a neighborhood search method based on geodesic distances, improved the rationality of the neighborhood. In addition, feature information was retained via the superpoint sets. Considering the practical requirements of large-scale 3D datasets, this study offers a robust and efficient segmentation method that combines traditional random forest and Markov random field models to segment 3D scene semantics. To address the need for modeling individual and unique buildings, our methodology utilized 3D mesh data of buildings as a data source for specific contour extraction. Model monomer construction and building contour extractions were based on mesh model slices and assessments of geometric similarity, which allowed the simultaneous and automatic achievement of these two processes.

Fast reconstruction of scanning transmission electron microscopy images using Markov random field model

In this study, we proposed a fast method of reconstruction for scanning transmission electron microscopy images. The proposed method is based on the Markov random field model and Bayesian inference, and we found that the method can reconstruct such images of sizes 512 × 512 and 264 × 240 in less than 200 ms and 100 ms, respectively. Furthermore, we showed that the method of reconstruction from multiple images without averaging them has better reconstruction performance than that from the averaged image.

Light Pollution Index System Model Based on Markov Random Field

Light pollution is one of the environmental pollution problems facing the world. The research on the measurement standard of light pollution is not perfect at present. In this paper, we proposed a Markov random field model to determine the light pollution risk level of a site. Firstly, the specific data of 12 indicators of 5 typical cities were collected, and 10-factor indicators were screened using the R-type clustering algorithm. Then, the entropy weight method was used to determine the weight, and the light pollution measurement method of the Markov random field was established. The model was tested by five different data sets, and the test results show that the model is very effective. Three kinds of potential effects were proposed, and the relationship between the factor index and potential effects was established by using the partial least square method. Three possible intervention strategies for solving the problem of light pollution are pointed out: road lighting system planning, increasing vegetation coverage, and building system planning. Finally, a simulated annealing algorithm was used to determine the best intervention strategy, concluding that using strategy 1 in urban neighborhood 2 was the most effective measure, reducing the risk level of light pollution by 17.2%.

Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a Markov random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

Study the effect of MRF Model based NC classifier with different distance measures and parameters

The accuracy of satellite image classification and the computational complexity is reduced due to the image's noisy pixels. Therefore, spatial contextual information-based classifiers are required to handle the noisy pixels and obtain the neighborhood information. This paper represents Noise clustering (NC) based Markov Random Field (MRF) models (SP, DA (H1, H2, H3, and H4)) that handle the noisy pixels and provide the information. The Smoothing Prior (SP) and Discontinuity Adaptive (DA) models are useful for reducing noise by smoothing the images and showing the boundary of classes, respectively. This study has carried out a comparative study among MRF model-based NC classifiers SP and DA for different distance measures and parameters. MRF models based on NC classifiers were tested for classifying Eucalyptus, Water, Riverine sand, Grassland, Dense Forest, and Wheat classes using the Formosat-2 and Landsat-8 multispectral images of the Haridwar area. The DA (H1) model provides the best overall accuracy (85.09%) for m=1.3, λ=0.2, δ=104,γ=0.8, and Mean Absolute Difference.

PRNU-based Image Forgery Localization with Deep Multi-scale Fusion

Photo-response non-uniformity (PRNU), as a class of device fingerprint, plays a key role in the forgery detection/localization for visual media. The state-of-the-art PRNU-based forensics methods generally rely on the multi-scale trace analysis and result fusion, with Markov random field model. However, such hand-crafted strategies are difficult to provide satisfactory multi-scale decision, exhibiting a high false-positive rate. Motivated by this, we propose an end-to-end multi-scale decision fusion strategy, where a mapping from multi-scale forgery probabilities to binary decision is achieved by a supervised deep fully connected neural network. As the first time, the deep learning technology is employed in PRNU-based forensics for more flexible and reliable integration of multi-scale information. The benchmark experiments exhibit the state-of-the-art accuracy performance of our method in both pixel-level and image-level, especially for false positives. Additional robustness experiments also demonstrate the benefits of the proposed method in resisting noise and compression attacks.

Markov random field model and expectation of maximization for images segmentation

Image segmentation is a significant issue in image processing. Among the various models and approaches that have been developed, some are commonly used the Markov Random Field (MRF) model, statistical techniques (MRF). In this study a Markov random field proposed is based on an EM Modified (EMM) model. In this paper, The local optimization is based on a modified Expectation-Maximization (EM) method for parameter estimation and the ICM method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry out a comparative study to find the best result. The effectiveness of our proposed methods has been proven by experimentation. We have applied this segmented algorithm to different types of images, exhibiting the algorithm's image segmentation strength with its best values criteria for EM statics and other methods.

Data-driven subsurface modelling using a Markov random field model

ABSTRACT This paper presents a method of subsurface modelling based on a Markov random field (MRF) model called Potts model. Potts model is an undirected graphical model and has been applied in image processing such as image denoising, restoration and inpainting. The proposed method is simple and requires only a few borehole data on soil types in both training and inference stages. Current implementations of the Potts model require substantial data for training, and they are not suitable for subsurface modelling. The proposed method was demonstrated through numerical examples for 2D and 3D virtual grounds and a real case history. In the numerical examples, the effect of the number of training datasets on the estimation results was also investigated. The proposed method can provide not only the most probable inference of subsurface model but also the spatial distribution of geological uncertainty and is compatible with reliability-based analysis in geotechnical engineering. The spatial distribution of uncertainty is informative in its own right. It directs the engineer to focus on mechanically important zones where the critical failure mechanism passes through if they coincide with the low-accuracy zones.

Identifying Wet and Dry Snow With Dual-Polarized C-Band SAR Data Based on Markov Random Field Model

Quad-pol SAR is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H-α decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy and kappa coefficient of 84.5% and 0.58, respectively.

Hierarchical Belief Propagation on Image Segmentation Pyramid.

The Markov random field (MRF) for stereo matching can be solved using belief propagation (BP). However, the solution space grows significantly with the introduction of high-resolution stereo images and 3D plane labels, making the traditional BP algorithms impractical in inference time and convergence.We present an accurate and efficient hierarchical BP framework using the representation of the image segmentation pyramid (ISP). The pixel-level MRF can be solved by a top-down inference on the ISP. We design a hierarchy of MRF networks using the graph of superpixels at each ISP level. From the highest/image to the lowest/pixel level, the MRF models can be efficiently inferred with constant global guidance using the optimal labels of the previous level. The large texture-less regions can be handled effectively by the MRF model on a high level. The advanced 3D continuous labels and a novel support-points regularization are integrated into our framework for stereo matching. We provide a data-level parallelism implementation which is orders of magnitude faster than the best graph cuts (GC) algorithm. The proposed framework, HBP-ISP, outperforms the best GC algorithm on the Middlebury stereo matching benchmark.

Political Improved Invasive Weed Optimization-Driven Hybrid Exemplar Technique for Video Inpainting Process

Video inpainting aspires to fill the Spatio-temporal holes in videos with probable and coherent content. This process recovers the missing content of corrupted video effectively, which is useful in many fields, including removal of watermarking and video restoration. The difficulties of creating video contents with exquisite detail while maintaining spatiotemporal coherence in the missing areas is the main difficulty in the video inpainting process. Modern studies ignore semantic structural coherence maintenance between frames in favor of using flow information to synthesize temporally smooth pixels. In this paper, Political Improved Invasive Weed Optimization (PIIWO)-based optimal exemplar is designed for the productive video inpainting process. Accordingly, the developed PIIWO algorithm is newly designed by combining Political Optimizer (PO) and Improved Invasive Weed Optimization (IIWO). Here, the inpainting results obtained from context-aware Ant Lion Gray Wolf Optimization (ALGWO)-based Markov Random Field (MRF) modeling, Whale Monarch Butterfly Optimization (Whale MBO)-based Deep Convolutional Neural Network (DCNN), K-Nearest Neighbors (KNN) with Bhattacharya distance, Bi-harmonic function modules and developed PIIWO-based exemplar model are fused using Bayes probabilistic fusion for producing the final result. Three metrics, peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME) and structural similarity (SSIM) of 40.19[Formula: see text]dB, 78.07[Formula: see text]dB and 0.9857, respectively, are used to assess the performance of the developed video inpainting technique.

A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images

Convolutional neural network (CNN) has been widely used in semantic segmentation for remote sensing images, and it has achieved great success. Due to the diversity of the spatial distribution of terrestrial objects in remote sensing images, it is difficult to effectively learn general geographical laws and apply them to a specific image. To introduce geographical knowledge into the CNN model more effectively, a self-learning-update CNN model (SLU-CNN) is proposed in this letter. It learns the representation of specific spatial dependence among different objects according to the CNN result, and then incorporates it with the CNN result to make semantic inference available. The proposed method mainly involves two modules. First, geographical objects generated from the CNN result are used as inference units. Second, the spatial dependence between inference units is learned to build a specific adaptive geographical relationship. And then, it is embedded as an adaptive penalty term into an object-based Markov random field model to achieve the collaboration between the CNN result and the semantic inference. Our method provides a general data-knowledge dual-driven framework for the deep neural network. Experiments of the GID and Sentinel-2 datasets validate the effectiveness of the proposed method by comparing it with different state-of-the-art CNN methods.

Sparsity enhanced MRF algorithm for automatic object detection in GPR imagery.

This study addressed the problem of automated object detection from ground penetrating radar imaging (GPR), using the concept of sparse representation. The detection task is first formulated as a Markov random field (MRF) process. Then, we propose a novel detection algorithm by introducing the sparsity constraint to the standard MRF model. Specifically, the traditional approach finds it difficult to determine the central target due to the influence of different neighbors from the imaging area. As such, we introduce a domain search algorithm to overcome this issue and increase the accuracy of target detection. Additionally, in the standard MRF model, the Gibbs parameters are empirically predetermined and fixed during the detection process, yet those hyperparameters may have a significant effect on the performance of the detection. Accordingly, in this paper, Gibbs parameters are self-adaptive and fine-tuned using an iterative updating strategy followed the concept of sparse representation. Furthermore, the proposed algorithm has then been proven to have a strong convergence property theoretically. Finally, we verify the proposed method using a real-world dataset, with a set of ground penetrating radar antennas in three different transmitted frequencies (50 MHz, 200 MHz and 300 MHz). Experimental evaluations demonstrate the advantages of utilizing the proposed algorithm to detect objects in ground penetrating radar imagery, in comparison with four traditional detection algorithms.

Integrating cell interaction with transcription factors to obtain a robust gene panel for prognostic prediction and therapies in cholangiocarcinoma.

Objective: The efficacy of immunotherapy for cholangiocarcinoma (CCA) is blocked by a high degree of tumor heterogeneity. Cell communication contributes to heterogeneity in the tumor microenvironment. This study aimed to explore critical cell signaling and biomarkers induced via cell communication during immune exhaustion in CCA. Methods: We constructed empirical Bayes and Markov random field models eLBP to determine transcription factors, interacting genes, and associated signaling pathways involved in cell-cell communication using single-cell RNAseq data. We then analyzed the mechanism of immune exhaustion during CCA progression. Results: We found that VEGFA-positive macrophages with high levels of LGALS9 could interact with HAVCR2 to promote the exhaustion of CD8+ T cells in CCA. Transcription factors SPI1 and IRF1 can upregulate the expression of LGALS9 in VEGFA-positive macrophages. Subsequently, we obtained a panel containing 54 genes through the model, which identified subtype S2 with high expression of immune checkpoint genes that are suitable for immunotherapy. Moreover, we found that patients with subtype S2 with a higher mutation ratio of MUC16 had immune-exhausted genes, such as HAVCR2 and TIGIT. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model, which was designated the tumor microenvironment risk score (TMRS). Conclusion: Cell communication-related genes can be used as important markers for predicting patient prognosis and immunotherapy responses. The TMRS panel is a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA.

Factors at multiple scales drive parasite community structure.

Understanding how ecological communities are assembled remains a key goal of ecosystem ecology. Because communities are hierarchical, factors acting at multiple scales can contribute to patterns of community structure. Parasites provide a natural system to explore this idea, as they exist as discrete communities within host individuals, which are themselves part of a community and metacommunity. We aimed to understand the relative contribution of multi-scale drivers in parasite community assembly and assess how patterns at one level may mask those occurring at another. Specifically, we wanted to disentangle patterns caused by passive sampling from those determined by ecological drivers, and how these vary with scale. We applied a Markov Random Fields model and assessed measures of β-diversity and nestedness for 420 replicate parasite infracommunities (parasite assemblages in host individuals) across two freshwater mussel host species, three sites and two time periods, comparing our results to simulations from four different ecologically relevant null models. We showed that β-diversity between sites (explaining 25% of variation in parasite distribution) and host species (41%) is greater than expected, and β-diversity between individual hosts is smaller than expected, even after accounting for parasite prevalence and characteristics of host individuals. Furthermore, parasite communities were significantly less nested than expected once parasite prevalence and host characteristics were both accounted for, but more nested than expected otherwise, suggesting a degree of modularity at the within-host level that is masked if underlying host and parasite characteristics are not taken into account. The Markov Random Fields model provided evidence for possible competitive within-host parasite interactions, providing a mechanism for the observed infracommunity modularity. An integrative approach that examines factors at multiple scales is necessary to understand the composition of ecological communities. Furthermore, patterns at one level can alter the interpretation of ecologically important drivers at another if variation at higher scales is not accounted for.

Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels.

Reconstruction of optical parameter fields in graded refractive index media based on laser beam deflection and attenuation measurement.

Graded refractive index media (GRIM) are widely applied as special functional materials in many practical engineering fields. Accurate knowledge of the optical parameters is key to using GRIM. In this study, simultaneous reconstruction of the refractive index and absorption coefficient fields of GRIM based on laser beam deflection and attenuation measurement is studied. A set of rays from the given positions along the given directions transits GRIM, and the deflection and attenuation of rays at the exit boundary are recorded as measurement information. A two-step reconstruction strategy is proposed to reconstruct the refractive index and absorption coefficient fields. First, the refractive index field is reconstructed from the ray deflection measurement information. Then, the ray trajectory can be obtained by the Runge-Kutta ray tracing technique based on the reconstructed refractive index field. Afterwards, the absorption coefficient field is reconstructed from the ray attenuation measurement according to the Bouguer law. The regularization technique based on the generalized Gaussian Markov random field model is employed to improve the reconstruction results. All test results show that the two-step reconstruction strategy is accurate and can be regarded as a promising reconstruction technique.

Visual detection of residual cut tobacco based on multi-feature fusion and Markov random field

Image segmentation has been commonly used for segmenting regions of interest in the industrial field. However, segmenting small objects in a reflective background is a complex task that limits the performance of image segmentation in the industry. We take residual cut tobacco in industrial production as an object and propose an automatic detection method. This method overcomes the influence of reflective background and achieves accurate detection of small-area residual cut tobacco. The proposed method combines multi-feature fusion method and Markov random field model to construct a coarse-to-fine segmentation framework. The coarse part is used to eliminate the effect of specular highlights and specular reflection. The fine part is used to detect and clear small regions of residual cut tobacco that are wrongly segmented by the coarse part. We collect 2411 images in the real industrial production line for evaluation. The experimental results show that the proposed method yields an averaged precision and mean intersection over union of 95.85% and 86.71%, which outperforms some detection methods based on deep learning.