Markov Random Field Algorithm Research Articles

Detection of Surface Rocks and Small Craters in Permanently Shadowed Regions of the Lunar South Pole Based on YOLOv7 and Markov Random Field Algorithms in SAR Images

Excluding rough areas with surface rocks and craters is critical for the safety of landing missions, such as China’s Chang’e-7 mission, in the permanently shadowed region (PSR) of the lunar south pole. Binned digital elevation model (DEM) data can describe the undulating surface, but the DEM data can hardly detect surface rocks because of median-averaging. High-resolution images from a synthetic aperture radar (SAR) can be used to map discrete rocks and small craters according to their strong backscattering. This study utilizes the You Only Look Once version 7 (YOLOv7) tool to detect varying-sized craters in SAR images. It also employs the Markov random field (MRF) algorithm to identify surface rocks, which are usually difficult to detect in DEM data. The results are validated by optical images and DEM data in non-PSR. With the assistance of the DEM data, regions with slopes larger than 10° are excluded. YOLOv7 and MRF are applied to detect craters and rocky surfaces and exclude regions with steep slopes in the PSRs of craters Shoemaker, Slater, and Shackleton, respectively. This study proves SAR images are feasible in the selection of landing sites in the PSRs for future missions.

A Model for Urban Environment Instance Segmentation with Data Fusion.

Fine-grained urban environment instance segmentation is a fundamental and important task in the field of environment perception for autonomous vehicles. To address this goal, a model was designed with LiDAR pointcloud data and camera image data as the subject of study, and the reliability of the model was enhanced using dual fusion at the data level and feature level. By introducing the Markov Random Field algorithm, the Support Vector Machine classification results were optimized according to the spatial contextual linkage while providing the model with the prerequisite of the differentiation of similar but foreign objects, and the object classification and instance segmentation of 3D urban environments were completed by combining the Mean Shift. The dual fusion approach in this paper is a method for the deeper fusion of data from different sources, and the model, designed more accurately, describes the categories of items in the environment with a classification accuracy of 99.3%, and segments the different individuals into groups of the same kind of objects without instance labels. Moreover, our model does not have high computational resource and time cost requirements, and is a lightweight, efficient, and accurate instance segmentation model.

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.

Uncertainty quantification of metallic microstructures using principal image moments

The present work uses Markov Random Field (MRF) algorithm to construct large-scale and statistically-equivalent samples from small-scale experimental data of metallic microstructures. While the MRF method can build such digital material representations in large computational domains, its algorithmic stochasticity (epistemic uncertainty) causes variations in the resulting microstructural features, such as the texture and grain topology. This work addresses the effects of the epistemic uncertainty on homogenized mechanical properties by characterizing the variations in the microstructural features using a shape descriptor based on the concept of moment invariants. In particular, 2D and 3D synthetic microstructure data for Titanium-7wt%Aluminum (Ti-7Al) alloy is generated with the MRF method using smaller-scale 2D experimental data. To quantify the uncertainty of the reconstructed synthetic samples, a graphical method and five different metrics of statistical variability are developed. Next, the propagation of the microstructural uncertainty on homogenized properties is studied using an analytical uncertainty quantification (UQ) algorithm and Gaussian Process Regression (GPR).

Deep Learning-Based Correlation Analysis between Spine Surgery Lumbar Facet Joint and Lumbar Disc Herniation Using Magnetic Resonance Images

The research aimed at discussing the analytic function of convolutional neural network (CNN) algorithm-based magnetic resonance images (MRI) in the correlation between lumbar disc herniation (LDH) and angle and irregular variation of joint (IVJ) of lumbar facet-joint (LFJ). First, CNN-based MRI (CNNM) algorithm was constructed, and Markov random field (MRF) and fuzzy C-means (FCM) algorithms were introduced for comparison. Meanwhile, all patients received MRI examination of lumbar, and CNNM algorithm was adopted in MRI images. The results showed that the sensitivity, specificity, accuracy, and precision (98.53%, 93.65%, 99.56%, and 98.74%, respectively) of the CNNM algorithm were all superior to those of MRF algorithm (90.41%, 81.11%, 91.18%, and 91.13%, respectively) and of FCM algorithm (93.14%, 82.86%, 93.23%, and 93.08%, respectively) ( P < 0.05 ). Besides, the lumbar spine angles of L3-L4, L4-L5, and L5-S1 (6.03 ± 1.34°, 7.14 ± 1.18°, and 8.96 ± 3.26°, respectively) in the experimental group was obviously less than those in the control group (6.84 ± 1.15°, 9.85 ± 1.25°, and 17.34 ± 4.79°, respectively) ( P < 0.05 ). In the experimental group, there was irregular mutation of LFJ in 78 cases, while 8 cases suffered from irregular mutation of LFJ in the control group. The proportions of protrusion in L3/4, L4/5, and L5/S1 segments (11 cases, 53 cases, and 14 cases, respectively) was higher than that in the control group (1 case, 5 cases, and 2 cases, respectively) ( P < 0.05 ). In short, the constructed CNNM algorithm had excellent performance in diagnosing lumbar MRI images and had clinical research and promotion value. Moreover, the IVJ of patients with LDH was notably increased, most of the physiological angle of the lumbar spine changed, and facet joint was correlated with the occurrence of LDH.

A novel unsupervised forest change detection method based on the integration of a multiresolution singular value decomposition fusion and an edge-aware Markov Random Field algorithm

ABSTRACT As a leading natural wealth, forests play an essential role in the development and prosperity of countries. Hence, monitoring their changes can lead to proper management and planning in conserving these resources. This study presents a novel unsupervised forest change detection method comprising two main steps: (1) generating a reliable difference image, i.e. sensitive to forest changes, and (2) producing a change map in which forest changes and their details (e.g. edges) are well characterized. In step (1), the vegetation indices- and spectral-based difference images were first calculated using a novel weighted angular operator. Afterwards, the difference images were combined using the 2D-multiresolution singular value decomposition (2D-MSVD) fusion approach to generate a noise-resistant difference image, in which forest changes are accurately highlighted. In step (2), the expectation-maximization gaussian mixture model (EMGMM) was first applied to the fused difference image to reach an initial binary change map. Next, an edge-aware MRF (EAMRF) model was initialized by the EMGMM-derived change map and then was adopted to achieve the final change map. Experimental results were achieved by utilizing five bi-temporal images acquired by the Landsat 5 and 8, and Sentinel 2 satellite sensors. The results indicated the efficacy of the proposed fused difference image in reflecting the forest changes. Compared with the traditional MRF method, the boundaries and geometrical shapes of changed regions were well preserved in the change maps obtained by EAMRF. The edge penalty function embedded in EAMRF also made this model converge in less running time compared to the traditional MRF algorithm. Furthermore, EAMRF outperformed the other change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications. The source code of the proposed change detection method with some samples of the datasets has been made available on https://github.com/AminMohsenifar to support related future works in remote sensing.

DNA Microarray Image Segmentation Using Markov Random Field Algorithm

A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm

Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance.

Fuzzy C-Means Clustering Algorithm-Based Magnetic Resonance Imaging Image Segmentation for Analyzing the Effect of Edaravone on the Vascular Endothelial Function in Patients with Acute Cerebral Infarction.

This paper aimed to discuss the denoising ability of magnetic resonance imaging (MRI) images based on fuzzy C-means clustering (FCM) algorithm and the influence of Butylphthalide combined with Edaravone treatment on nerve function and vascular endothelial function in patients with acute cerebral infarction (ACI). Based on FCM algorithm, Markov Random Field (MRF) model algorithm was introduced to obtain a novel algorithm (NFCM), which was compared with FCM and MRF algorithm in terms of misclassification rate (MCR) and difference of Kappa index (KI). 90 patients with ACI diagnosed in hospital from December 2018 to December 2019 were selected as subjects, who were divided into combined treatment group (conventional treatment + Edaravone + Butylphthalide) and Edaravone group (conventional treatment + Edaravone) randomly, each consisting of 45 cases. The National Institutes of Health Stroke Scale (NIHSS) score and endothelial function index level such as plasma nitric oxide (NO), human endothelin-1 (ET-1), and vascular endothelial cell growth factor (VEGF) were compared before and after treatment between the two groups. The results showed that the MCR of NFCM was evidently inferior to FCM and MRF, and the KI was notably higher relative to the other two algorithms. After treatment, the NIHSS score of the combined treatment group was (9.09 ± 1.86) points and that of Edaravone group was (14.97 ± 3.44) points, with evident difference between the two groups (P < 0.05). After treatment, the NO of the combined treatment was (54.63 ± 4.85), and that of Edaravone group was (41.54 ± 5.27), which was considerably different (P < 0.01), and the VEGF and ET-1 of combined treatment group were greatly inferior to Edaravone group (P < 0.01). It was revealed that the novel algorithm based on FCM can obtain more favorable quality and segmentation accuracy of MRI images. Moreover, Butylphthalide combined with Edaravone treatment can effectively improve nerve function, vascular endothelial function, and short-term prognosis in ACI, which was safe and worthy of clinical adoption.

Intelligent Perception of Coseismic Landslide Migration Areas Along Sichuan–Tibet Railway

The geological conditions along Sichuan–Tibet Railway are complex, and frequently-occurred landslides have brought severe challenges to the railway construction. Therefore, a complete and accurate landslide perception can provide references for railway route selection and landslide risk governance. In this study, we utilized change vector analysis, principal component analysis, and independent component analysis (ICA) for change detection images generation, and then adopted the multithreshold method to produce the training sample templates for landslides and nonlandslides, respectively. The Markov random field (MRF) algorithm was further used to extract the optimal landslide objects. In particular, we tested the performance of the proposed approach using the Sentinel-2 datasets in a rapid perception of the coseismic landslides for the Nyingchi event that occurred on 18 November 2017 and affected the railway construction. We further calculated completeness, correctness, accuracy, F1-score, and Kappa coefficient, for a quantitative evaluation of landslide perception results. We found that the ICA-based change detection in MRF can extract landslides more completely and accurately. This study set up with the aim to assess the effectiveness and applicability of the proposed method in mapping landslide migration areas under complex geological conditions along the Sichuan–Tibet Railway, which offers a comprehensively intelligent approach to supporting the hazard mitigation for a safe railway construction and operation.

Laser scanner measuring improved by image pixels using a Markov random field algorithm.

Laser scanners can be employed for spatial measuring tasks, but measuring accuracy is restricted because of the time of flight working principle. Laser-scanner-based observations with measuring errors might lead to rough spatial reconstruction. In this paper, an image registration method applying a Markov random field (MRF) algorithm is proposed. First, point cloud images are projected to a particular plane in a specific way. Then, the characteristic points of the projected image and the color image are extracted by an improved Harris algorithm. Next, the rotation and translation matrices can be calculated from the two image planes through the registration method. Finally, the MRF model is established describing the relation between the pixels and corresponding point cloud, which improves the resolution of the point cloud image. Furthermore, the color information of the point cloud is also matched. This method improves the efficiency and accuracy of registration. The final experimental result shows that using the MRF model increases measuring accuracy by 15%.

Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global

Semantic interpretation of the 3D scene is one of the most challenging problems in point cloud processing, which also deems as an essential task in a wide variety of point cloud applications. The core task of semantic interpretation is semantic labeling, namely, obtaining a unique semantic label for each point in the point cloud. Despite several reported approaches, semantic labeling continues to be a challenge owing to the complexity of scenes, objects of various scales, and the non-homogeneity of unevenly distributed points. In this paper, we propose a novel method for obtaining semantic labels of airborne laser scanning (ALS) point clouds involving the embedding of local context information for each point with multi-scale deep learning, nonlinear manifold learning for feature dimension reduction, and global graph-based optimization for refining the classification results. Specifically, we address the tasks of learning discriminative features and global labeling smoothing. The key contribution of our study is threefold. First, a hierarchical data augmentation strategy is applied to enhance the learning of deep features based on the PointNet++ network and simultaneously eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. Subsequently, the learned hierarchical deep features are globally optimized and embedded into a low-dimensional space with a nonlinear manifold-based joint learning method with the removal of redundant and disturbing information. Finally, a graph-structured optimization based on the Markov random fields algorithm is performed to achieve global optimization of the initial classification results that are obtained using the embedded deep features by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. We conducted thorough experiments on ALS point cloud datasets to assess the performance of our framework. Results indicate that compared to other commonly used advanced classification methods, our method can achieve high classification accuracy. The overall accuracy (OA) of our approach on the ISPRS benchmark dataset can scale up to 83.2% for classifying nine semantic classes, thereby outperforming other compared point-based strategies. Additionally, we evaluated our framework on a selected portion of the AHN3 dataset, which provided OA up to 91.2%.

Fast High-Order Sparse Subspace Clustering With Cumulative MRF for Hyperspectral Images

Sparse subspace clustering (SSC), as a powerful tool in hyperspectral image (HSI) segmentation, has caused widespread attention recently. However, the existing methods are generally affected by the limitations of region consistency and time complexity. To address these issues, we propose a fast high-order SSC (FHoSSC) with the cumulative Markov random field (MRF) algorithm in this letter. By capturing high-order information, we explore the pixel-level contextual restraints within the same high-order data to preserve consistency. Also, a new regularization term is introduced by the spatial constraints among adjacent high-order data for improving segmentation accuracy. Finally, cumulative MRF, as a variant of MRF, is used to further refine the segmentation result through combining original HSI information. Experiments on real data sets demonstrate that the proposed method not only outperforms the state-of-the-art methods in segmentation accuracy but also reduces the time complexity significantly.

Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm.

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

Do Epistemic Uncertainties Allow for Replacing Microstructural Experiments with Reconstruction Algorithms?

A novel problem in computational materials modeling is addressed: “Are the computational microstructure reconstruction techniques reliable enough to replace experiments?” Here, “reliable” computations are associated with producing “expected” reconstructions that are adequately close to the experimental data. The output of computational techniques can deviate from the experimental measurements because of the epistemic uncertainties in the algorithms. In this work, an analytical formulation for quantification of epistemic uncertainties in a microstructure reconstruction algorithm based on Markov random field is presented. The method is used to predict the large-scale spatial distribution of a microstructure given an experimental input measured over a small spatial domain. However, small variations are observed on the microstructural features of the synthesized samples due to the Markov random field algorithm. The proposed analytical technique aims to quantify these uncertainties and estimate their propagation to the macroscale material properties to provide a significant understanding on how reliable it is to replace the experiments with the Markov random field model to predict microstructural maps over large spatial domains.

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images.

Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

Railway equipment detection using exact height function shape descriptor based on fast adaptive Markov random field

This paper proposes a hierarchical feature-matching model for the typical faults detection, which is a big challenge in the trouble of a moving freight car detection system (TFDS) due to the constant color and complex background of images. The proposed model divides fault detection into two stages: image segmentation and parallel shape matching. In the process of segmentation, a fast adaptive Markov random field (FAMRF) algorithm is presented based on the image pyramid model and affinity propagation theory. In the process of shape matching, a shape descriptor named exact height function (EHF) is introduced on the basis of parallel dynamic programming. The experimental results indicate that the proposed hierarchical model combined with FAMRF and EHF can achieve automatic detection of an air brake system, bogie block key, and fastening bolt. The proposed model achieves high detection accuracy and great robustness, and it can be effectively applied to the fault detection in TFDS.

Interferometric synthetic aperture radar phase unwrapping based on sparse Markov random fields by graph cuts

Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) data. It is known that two-dimensional (2-D) PU problems can be formulated as maximum a posteriori estimation of Markov random fields (MRFs). However, considering that the traditional MRF algorithm is usually defined on a rectangular grid, it fails easily if large parts of the wrapped data are dominated by noise caused by large low-coherence area or rapid-topography variation. A PU solution based on sparse MRF is presented to extend the traditional MRF algorithm to deal with sparse data, which allows the unwrapping of InSAR data dominated by high phase noise. To speed up the graph cuts algorithm for sparse MRF, we designed dual elementary graphs and merged them to obtain the Delaunay triangle graph, which is used to minimize the energy function efficiently. The experiments on simulated and real data, compared with other existing algorithms, both confirm the effectiveness of the proposed MRF approach, which suffers less from decorrelation effects caused by large low-coherence area or rapid-topography variation.

Two-Stage Road Terrain Identification Approach for Land Vehicles Using Feature-Based and Markov Random Field Algorithm

Road terrain identification is one of the important tasks for driving assistant systems or autonomous land vehicles. It plays a key role in improving driving strategy and enhancing fuel efficiency. In this paper, a two-stage approach using multiple sensors is presented. In the first stage, a feature-based identification approach is performed using an accelerometer, a camera, and downward-looking and forward-looking laser range finders (LRFs). This produces four classification label sequences. In the second stage, a majority vote is implemented for each label sequences to match them into synchronized road patches. Then a Markov Random Field (MRF) model is designed to generate the final optimized identification results to improve the forward-looking LRF. This approach enables the vehicle to observe the upcoming road terrain before moving onto it by fusing all the classification results using an MRF algorithm. The experiments show this approach improved the terrain identification accuracy and robustness significantly for some familiar road terrains.

Segmentation of brain MR images using a proper combination of DCS based method with MRF

Manual segmentation of Magnetic Resonance Images (MRI) is a time-consuming process, thus automatic segmentation of brain MR images has attracted more attention in recent years. In this paper, we introduce Dynamic Classifier Selection Markov Random Field (DCSMRF) algorithm for supervised segmentation of brain MR images into three main tissues such as White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). DCSMRF combines a novel ensemble method with the Markov Random Field (MRF) algorithm and tries to obtain the advantages of both algorithms. For the ensemble part of DCSMRF, we propose an ensemble method called Dynamic Classifier System-Weighted Local Accuracy (DCS-WLA) which is a type of Combination of Multiple Classifier (CMC) algorithm. Later, the MRF algorithm is utilized for incorporating spatial, contextual and textural information in this paper. For the MRF section, an energy function based on the output of the DCS-WLA algorithm is proposed, then maximum value for Maximum A Posterior (MAP) criterion is searched to obtain optimal segmentation. The MRF algorithm applies similar to a post processing step in which only a subset of pixels is selected for optimization step. Hence, a vast amount of search space is pruned. Consequently, the computational burden of the proposed algorithm is more tolerable than the conventional MRF-based methods. Moreover, by employing ensemble algorithms, the accuracy and reliability of final results are enhanced compared to the individual methods.