Showing papers on "Markov random field published in 2022"
TL;DR: In this article , the depth estimation method of the defocused image based on a Markov random field and the method based on geometric constraints were proposed to solve the problem of depth estimation of defiant images in microscopic scenes.
Abstract: When using a monocular camera for detection or observation, one only obtain two-dimensional information, which is far from adequate for surgical robot manipulation and workpiece detection. Therefore, at this scale, obtaining three-dimensional information of the observed object, especially the depth information estimation of the surface points of each object, has become a key issue. This paper proposes two methods to solve the problem of depth estimation of defiant images in microscopic scenes. These are the depth estimation method of the defocused image based on a Markov random field, and the method based on geometric constraints. According to the real aperture imaging principle, the geometric constraints on the relative defocus parameters of the point spread function are derived, which improves the traditional iterative method and improves the algorithm’s efficiency.
47 citations
TL;DR: This paper reduces the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method, which yields better images than those from the existing state-of-the-art-methods.
Abstract: : Image dehazing is still an open research topic that has been under-going a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method. The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions. This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks (CNNs) with a Parallax Attention Mechanism (PAM). It uses the differential pixel-based variance in order to estimate transmittance. The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map. The transmission map is also improved based on improved Markov random field (MRF) energy functions. We used different images to test the proposed algorithm. The entropy value of the proposed method was 7.43 and 7.39, a percent increase of (cid:2) 4.35% and (cid:2) 5.42%, respectively, compared to the best existing results. The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics. The proposed approach’s drawback, an over-reliance on real ground truth images, is also investigated. The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.
18 citations
TL;DR: This paper uses the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images and offers an effective hybrid model that is based on the concept of feature-level fusion.
Abstract: The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and remote sensing scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), have made outstanding accomplishments. Deep feature extraction from a CNN model is a frequently utilized technique in these approaches. Although CNN-based techniques have achieved considerable success, there is indeed ample space for improvement in terms of their classification accuracies. Certainly, fusion with other features has the potential to extensively improve the performance of distant imaging scene classification. This paper, thus, offers an effective hybrid model that is based on the concept of feature-level fusion. We use the fuzzy C-means segmentation technique to appropriately classify various objects in the remote sensing images. The segmented regions of the image are then labeled using a Markov random field (MRF). After the segmentation and labeling of the objects, classical and CNN features are extracted and combined to classify the objects. After categorizing the objects, object-to-object relations are studied. Finally, these objects are transmitted to a fully convolutional network (FCN) for scene classification along with their relationship triplets. The experimental evaluation of three publicly available standard datasets reveals the phenomenal performance of the proposed system.
15 citations
TL;DR: In this paper , a coarse-to-fine Markov random field (MRF) method was proposed for ground segmentation in real-time with only one thread of the I7-8700 CPU.
Abstract: Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs. However, the existing ground segmentation methods are very difficult to balance accuracy and computational complexity. This paper proposes a fast point cloud ground segmentation approach based on a coarse-to-fine Markov random field (MRF) method. The method uses the coarse segmentation result of an improved local feature extraction algorithm instead of prior knowledge to initialize an MRF model. It provides an initial value for the fine segmentation and dramatically reduces the computational complexity. The graph cut method is then used to minimize the proposed model to achieve fine segmentation. Experiments on two public datasets and field tests show that our approach is more accurate than both methods based on features and MRF and faster than graph-based methods. It can process Velodyne HDL-64E data frames in real-time (24.86 ms, on average) with only one thread of the I7-8700 CPU. Compared with methods based on deep learning, it has better environmental adaptability.
14 citations
DOI•
01 Jan 2022
TL;DR: There are many different approaches to solve the inpainting problem such as feature distribution, sparse representation, Markov random field, multiscale graph cuts, neural networks, and GAN-based methods.
Abstract: Inpainting is the ancient art technique of modifying the image when it can’t be detected. This current study discusses the various approaches in image inpainting and compares the methods with their time of detection and accuracy. There are many different approaches to solve the inpainting problem. These approaches such as feature distribution, sparse representation, Markov random field, multiscale graph cuts, neural networks, and GAN-based methods are studied. The limitations that are imposed on the regions of the images to be inpainted are studied in the current work. The applications of such approaches are discussed in brief.
14 citations
TL;DR: This paper constructs a power line data set using UAV images and classify the data according to the image clutter and proposes a method combining line detection and semantic segmentation, which shows better performance on images with different IC.
Abstract: Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.
12 citations
TL;DR: Wang et al. as mentioned in this paper proposed an SAR image segmentation algorithm based on constrained smoothing and hierarchical label correction (CSHLC), where a Canny algorithm is used to extract the edges of SAR images, and the Gaussian smoothing is performed on SAR images under edge constraints to achieve noise reduction.
Abstract: Synthetic aperture radar (SAR) is widely used in the field of modern remote sensing due to its high resolution for a comparatively small antenna. However, there are still some difficulties in the processing of SAR images. In particular, accurate segmentation of small targets and image corners remains an important challenge, as these can easily be lost during conventional image smoothing and denoising methods. To address this, we propose an SAR image segmentation algorithm based on constrained smoothing and hierarchical label correction (CSHLC). First, a Canny algorithm is used to extract the edges of SAR images, and the Gaussian smoothing is performed on SAR images under edge constraints to achieve noise reduction so that the edges of small and big targets are well preserved. Second, a preliminary K-means clustering is conducted on the smoothing results, and then, a Markov random field (MRF) model is used on the clustering results (“original label” results), iteratively calculating a maximum likelihood set of pixel labels. Finally, through two label correction methods, pixel group counting comparison (PGCC) and gray similarity comparison (GSC), the labels of the MRF output are further checked and corrected to obtain final segmentation results. Compared with seven state-of-the-art algorithms, simulation results on both simulated SAR images and real SAR images show that the proposed CSHLC delivers higher accuracy while better retaining corners and small targets.
7 citations
09 Jun 2022
TL;DR: This work proposes SVGA (Structured Variational Graph Autoencoder), an accurate method for feature estimation that combines the advantages of probabilistic inference and graph neural networks, achieving state-of-the-art performance in real datasets.
Abstract: Given a graph with partial observations of node features, how can we estimate the missing features accurately? Feature estimation is a crucial problem for analyzing real-world graphs whose features are commonly missing during the data collection process. Accurate estimation not only provides diverse information of nodes but also supports the inference of graph neural networks that require the full observation of node features. However, designing an effective approach for estimating high-dimensional features is challenging, since it requires an estimator to have large representation power, increasing the risk of overfitting. In this work, we propose SVGA (Structured Variational Graph Autoencoder), an accurate method for feature estimation. SVGA applies strong regularization to the distribution of latent variables by structured variational inference, which models the prior of variables as Gaussian Markov random field based on the graph structure. As a result, SVGA combines the advantages of probabilistic inference and graph neural networks, achieving state-of-the-art performance in real datasets.
6 citations
TL;DR: Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed in this article to segment polyps in colonoscopic images.
5 citations
TL;DR: A novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system is described, outperforming the current deep learning as well as features-based detecting DR approaches.
Abstract: Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.
5 citations
TL;DR: In this article , an unsupervised change detection method was proposed by optimizing two critical steps, i.e., the generation and analysis of difference image, and the change map is obtained by the improved Markov random field which takes the difference in the neighborhood pixel values into account.
Abstract: Change detection is a research hotspot in the remote sensing field. In this letter, an unsupervised change detection method was proposed by optimizing two critical steps, i.e., the generation and analysis of difference image. First, the difference vectors of features are calculated using the simple differencing method. Some changed and unchanged pixels are generated by the majority voting on the results produced by clustering the difference vectors and then are used for the weight calculation of difference vectors. The weights are calculated by means of F-Score and considered in the weighted change vector analysis to produce a discriminative difference image. Finally, the change map is obtained by the improved Markov random field which takes the difference in the neighborhood pixel values into account. Experimental results on three data sets demonstrated that the proposed method outperformed six unsupervised change detection methods in terms of overall accuracy.
TL;DR: In this paper , an unsupervised and graph-based method of image segmentation is proposed, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal.
TL;DR: SMURF as discussed by the authors is a differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion.
Abstract: Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for.Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood.Our code and examples are available at: https://github.com/spetti/SMURF.Supplementary data are available at Bioinformatics online.
TL;DR: In this paper , a method combining Markov Random Field and Spectral Similarity Measure (MRF-SSM) is proposed by using Sentinel-1 A time-series images.
Abstract: Crop planting area mapping is essential for crop phenology monitoring, yield prediction, and disaster prevention. In this study, a winter wheat identification method combining Markov Random Field and Spectral Similarity Measure (MRF-SSM) is proposed by using Sentinel-1 A time-series images. It is found that compared with VH polarization, the backscattering coefficient of winter wheat at VV polarization fluctuates more at all growth stages and is used for winter wheat mapping. The result shows that the precision of mapping winter wheat using the MRF-SSM is 89.62% which is higher than using the support vector machine (SVM) and random forest (RF) methods. Because winter wheat near towns can be accurately identified using MRF-SSM methods. Moreover, the MRF-SSM method has the advantages of fewer winter wheat samples and less computation time. Therefore, time-series Sentinel-1A images with MRF-SSM have great potential for mapping winter wheat or other crops.
TL;DR: Wang et al. as discussed by the authors proposed an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means clustering and Markov random field modeling into a level set formulation.
Abstract: 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.
TL;DR: In this article , the authors proposed a method for the detection of localization failures using Markov random fields with fully connected latent variables, which enables to take the entire relation into account and contributes to the exact misalignment recognition.
Abstract: Most of the recent automated driving systems assume the accurate functioning of localization. Unanticipated errors cause localization failures and result in failures in automated driving. An exact localization failure detection is necessary to ensure safety in automated driving; however, detection of the localization failures is challenging because sensor measurement is assumed to be independent of each other in the localization process. Owing to the assumption, the entire relation of the sensor measurement is ignored. Consequently, it is difficult to recognize the misalignment between the sensor measurement and the map when partial sensor measurement overlaps with the map. This paper proposes a method for the detection of localization failures using Markov random fields with fully connected latent variables. The full connection enables to take the entire relation into account and contributes to the exact misalignment recognition. Additionally, this paper presents localization failure probability calculation and efficient distance field representation methods. We evaluate the proposed method using two types of datasets. The first dataset is the SemanticKITTI dataset, whereby four methods are compared with the proposed method. The comparison results reveal that the proposed method achieves the most accurate failure detection. The second dataset is created based on log data acquired from the demonstrations that we conducted in Japanese public roads. The dataset includes several localization failure scenes. We apply the failure detection methods to the dataset and confirm that the proposed method achieves exact and immediate failure detection.
TL;DR: A conditional random field (CRF) based model is proposed for polarimetric SAR data along with Wishart and Wishart mixture model (WMM) classifiers, namely Wishart-CRF and WMM- CRF, to perform the classification and the model exhibits better classification results by significantly reducing the noise and preserving the finer details of edges and small regions.
Abstract: ABSTRACT Classification of polarimetric SAR images into different ground covers has important applications in fields such as land mapping, agriculture monitoring, and assessment. The Wishart supervised classifier is one of the most widely used and general purpose classifier for polarimetric SAR data. However, it is a pixel-based classifier, so the performance is greatly affected by inherent speckle noise. The impact of speckle noise can be reduced by considering the spatial information from neighbouring pixels for classification tasks. In this paper, we aim to improve classification results by incorporating spatial-contextual information along with preservation of significant details such as edges and micro-regions. For this purpose, a conditional random field (CRF) based model is proposed for polarimetric SAR data along with Wishart and Wishart mixture model (WMM) classifiers, namely Wishart-CRF and WMM-CRF, to perform the classification. The model is compared with the Markov random field (MRF) based model as well as neural network-based models. The results are analysed in terms of accuracy and preservation of details such as edges and micro-regions. The model is assessed using three full polarimetric SAR benchmark data sets. The CRF model exhibits better classification results by significantly reducing the noise and preserving the finer details of edges and small regions.
TL;DR: In this paper , a high-resolution remote-sensing (RS) image compression framework was proposed by incorporating Markov random field (MRF)-oriented attention into the attention mechanism, which can accelerate the convergence in the training of deep neural networks (DNNs), thus facilitating deploying it on resource-limited IOT devices.
Abstract: Content-weighted compression scheme for high-resolution remote-sensing (RS) images can be well modeled by Markov random field (MRF)-oriented attention. This article addresses high-resolution RS image compression by incorporating MRF into attention mechanism. To this end, we reformulate the attention mechanism with MRF-based probabilistic graph modeling implicitly and combine the target of image compression and parameter learning of MRF in a unified framework, namely high-order MRF-oriented attention (HMA) network. Specifically, HMA extends key-value query (KVQ) pairwise terms of the vanilla attention to high-order terms, by which the prior information could be expressed effectively to boost performance of high-resolution RS image compression. It is noted that several superiorities of HMA are listed. First, unlike the vanilla attention network that apt to yield coarse features, HMA is capable of output more pleasing decoding results. Second, HMA can accelerate the convergence in the training of the deep neural networks (DNNs), thus facilitating deploying it on resource-limited IOT devices. Third, HMA demonstrates its potential of processing semantic joint task. Moreover, We thoroughly evaluate our approach on standard data sets of varying resolutions, the proposed framework performs favorably against most image coding standards and DNN-based codecs on the ISPRS Vaihingen data set and the USC-SIPI data set especially at low bit rates.
TL;DR: Wang et al. as mentioned in this paper formulated the structural segmentation as a Markov random field (MRF) labeling problem and proposed a new clustering algorithm to build superfacets by incorporating 3D local geometric information.
Abstract: Recognizing and fitting shape primitives from underlying 3D models are key components of many computer graphics and computer vision applications. Although a vast number of structural recovery methods are available, they usually fail to identify blending surfaces, which corresponds to small transitional regions among relatively large primary patches. To address this issue, we present a novel approach for automatic segmentation and surface fitting with accurate geometric parameters from 3D models, especially mechanical parts. Overall, we formulate the structural segmentation as a Markov random field (MRF) labeling problem. In contrast to existing techniques, we first propose a new clustering algorithm to build superfacets by incorporating 3D local geometric information. This algorithm extracts the general quadric and rolling-ball blending regions, and improves the robustness of further segmentation. Next, we apply a specially designed MRF framework to efficiently partition the original model into different meaningful patches of known surface types by defining the multilabel energy function on the superfacets. Furthermore, we present an iterative optimization algorithm based on skeleton extraction to fit rolling-ball blending patches by recovering the parameters of the rolling center trajectories and ball radius. Experiments on different complex models demonstrate the effectiveness and robustness of the proposed method, and the superiority of our method is also verified through comparisons with state-of-the-art approaches. We further apply our algorithm in applications such as mesh editing by changing the radius of the rolling balls.
TL;DR: In this paper , a novel PolSAR image classification method that removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via the Markov random field (MRF) is presented.
Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method that removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via the Markov random field (MRF). Especially, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying a convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR data sets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.
TL;DR: In this paper , the authors proposed two types of graphical models, i.e., CRF-MSDA and Markov Random Field for MSDA, for cross-domain joint modeling and learnable domain combination.
Abstract: Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation. In this problem, it is essential to model multiple source domains and target domain jointly, and an effective domain combination scheme is also highly required. The graphical structure among different domains is useful to tackle these challenges, in which the interdependency among various instances/categories can be effectively modeled. In this work, we propose two types of graphical models, i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random Field for MSDA (MRF-MSDA), for cross-domain joint modeling and learnable domain combination. In a nutshell, given an observation set composed of a query sample and the semantic prototypes (i.e. representative category embeddings) on various domains, the CRF-MSDA model seeks to learn the joint distribution of labels conditioned on the observations. We attain this goal by constructing a relational graph with all observations and conducting local message passing on it.
TL;DR: This work proposes a method for learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) on a robot as POMCP is used to plan future actions, and presents an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRf.
Abstract: We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions. Then, we present an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRF. Our approach acquires information from the agent’s action outcomes to adapt online the MRF if a mismatch is detected between the MRF and the true state. We test this technique on two domains, rocksample, a standard rover exploration task, and a problem of velocity regulation in industrial mobile robotic platforms, showing that the MRF adaptation algorithm improves the planning performance with respect to the standard approach, which does not adapt the MRF online. Finally, a ROS-based architecture is proposed, which allows running the MRF learning, the MRF adaptation, and MRF usage in POMCP on real robotic platforms. In this case, we successfully tested the architecture on a Gazebo simulator of rocksample. A video of the experiments is available in the Supplementary Material, and the code of the ROS-based architecture is available online.
TL;DR: In this article , a superpixel-correlation-based multiview classification approach is proposed for hyperspectral image classification, where a random forest classifier in conjunction with Markov random field regularization is used as the backend classifier of each view.
Abstract: Hyperspectral images provide plentiful spectral–spatial information regarding the nature of different materials, leading to the potential of more efficient detection in diverse areas. However, the high volume of spectral bands, along with limited reference data, leads to many challenges. To alleviate these issues, we propose a superpixel-correlation-based multiview classification approach. Here, the spectral–spatial multiple views are generated via multiscale superpixel segmentation and correlation-based spectral band clustering. A random forest classifier in conjunction with Markov random field regularization is used as the backend classifier of each view. In particular, an energy function with improved metrics of smoothness is introduced. For decision fusion, the pixelwise weight maps of the views are generated based on both classification certainty and neighboring smoothness. The proposed approach is evaluated on three widely used hyperspectral data sets, and the experimental results demonstrate that the proposed method can achieve a competitive performance compared with other existing methods.
TL;DR: In this paper , a non-additive image steganography framework based on Markov Random Fields (MRF) is proposed to penalize the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors.
Abstract: The majority of image steganography methods lie within the additive scheme that assumes the pixel modifications are independent. Whereas, adapting the embedding modifications based on the mutual embedding impact of neighboring pixels reduce changes in the higher-order statistics of the cover imposed by embedding. However, non-additive schemes are more challenging due to the lack of practical embedding codes that minimize an arbitrary distortion function. In this paper, we propose a general non-additive image steganographic framework based on Markov Random Fields (MRF) which generalizes a few existing research in many aspects and offers an elegant way to model the mutual dependencies between neighborhood modifications in terms of pairwise cliques. The proposed model satisfies spatial coherence by penalizing the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors. Mean Field (MF) inference is used to iteratively estimate the marginal probability at each pixel based on its neighbors so that the original interactions are information projected to the resulting marginals. MF replaces the values of variables with expectations so the result is compatible with practical embedding methods. The proposed framework can be applied to any additive scheme; the initial cost assignment is done by an additive method and MRF modeling defines a non-additive distortion related to statistical detectability that encourages the adjacent changes to synchronize. We study our framework in both symmetric and asymmetric schemes. In the symmetric scheme, a parallel MF is used as an adaptive filter to encourage synchronized embedding changes. The framework can upgrade to an asymmetric scheme that reduces the detectability by conditioning embedding based on the message using clamping exerted in a structural MF. Therefore, our framework is compatible with and generalizes existing non-additive methods, and as shown in experimental results, outperforms the state-of-the-art in both schemes.
TL;DR: In this article , an object-based Gaussian-Markov random field with gravity property parameters (OGMRF-GPP) method is proposed to solve the drawbacks of low accuracy and insufficient stability of extraction algorithms.
Abstract: The extraction of power line from aerial image with complex background is an extremely difficult task. In order to solve the drawbacks of low accuracy and insufficient stability of extraction algorithms, a novel object-based Gaussian–Markov random field with gravity property parameters (OGMRF-GPP) method is proposed. First of all, the OGMRF-GPP method extends the Gaussian–Markov model to a contactless and irregular neighborhood system. Secondly, the OGMRF-GPP method constructs a gravity property model by introducing multi-dimensional spatial correlations between line segments. The gravity property model can not only realize the high-precision adaptive estimation of the parameters of the linear regression equation, but also simplify the parameter estimation process. Finally, the power line segments extracted by OGMRF-GPP method can be fitted effectively. Experimental results show that the proposed method can extract power lines in a variety of scenes with higher accuracy compared with several other methods.
TL;DR: In this paper , a Markov Random Field (MRF) model was employed to identify cell-type-specific DE genes across conditions from scRNA-seq data, and the results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate.
Abstract: The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson's disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.
TL;DR: This paper proposes an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion–Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, [Formula: see text]-nearest neighbors (KNN) and the harmonic functions.
Abstract: Image inpainting removes unwanted objects from the image, signifying the original image restoration. Even though several techniques are introduced for image inpainting, but still, there are several challenging issues associated with the conventional methods regarding data loss, which are effectively handled based on the proposed approach. In this paper, we propose an effective hybrid image inpainting method that is termed as ALGDKH, which is the hybridization of Ant Lion–Gray Wolf Optimizer (ALG)-based Markov random field (MRF) modeling, deep learning, [Formula: see text]-nearest neighbors (KNN) and the harmonic functions. The crack input image is forwarded as an input to Markov random field modeling to obtain image inpainting, where the MRF energy is minimized based on the ALG. Then, the same crack image is subjected to the Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules to obtain the inpainting results. Finally, the results from the proposed ALG-based Markov random field modeling, Whale–MBO-based DCNN, KNN with Bhattacharya distance and Bi-harmonic function modules are fused through Bayes-probabilistic fusion for the final inpainting results. The proposed method produces the maximal PSNR of 38.14[Formula: see text]dB, maximal SDME of 75.70[Formula: see text]dB and the maximal SSIM of 0.983.
TL;DR: MARBLES is a novel statistical model for cross-condition DE gene detection from scRNA-seq data that employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability.
Abstract: Abstract The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson’s disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.
TL;DR: Li et al. as mentioned in this paper proposed an incremental-completion self-generated template (SGT) to reconstruct a deformable soft object with complete geometry and consistent texture by building a non-rigid registration that combines geometry and optical flow features, the SGT is dynamically updated and completed by supplementing the information from each initial state model.
Abstract: In reconstructing soft objects under different deformation states with RGB-D sensors, the results usually suffer from incomplete geometries and textures due to self-occlusion, such as dynamic wrinkles on a garment. A priori template is usually used for addressing this issue, but it requires complex scanning and an elaborate setup. This paper proposes a new framework to reconstruct a deformable soft object with complete geometry and consistent texture by introducing an incremental-completion self-generated template (SGT). By building a non-rigid registration that combines geometry and optical flow features, the SGT is dynamically updated and completed by supplementing the information from each initial state model. Then the updated SGT is reversely deformed to each state to obtain a sequence of dynamic reconstructed results with consistent geometry. Furthermore, a consistent Markov random field is also proposed to constrain mesh models in different states to generate consistent texture and guide non-rigid deformation. Experimental results show that our method achieves multi-state high-quality reconstruction effects, which provides a new solution for dynamically reconstructing colored soft objects.
TL;DR: Li et al. as discussed by the authors proposed an incremental-completion self-generated template (SGT) to reconstruct a deformable soft object with complete geometry and consistent texture by building a non-rigid registration that combines geometry and optical flow features, the SGT is dynamically updated and completed by supplementing the information from each initial state model.
Abstract: In reconstructing soft objects under different deformation states with RGB-D sensors, the results usually suffer from incomplete geometries and textures due to self-occlusion, such as dynamic wrinkles on a garment. A priori template is usually used for addressing this issue, but it requires complex scanning and an elaborate setup. This paper proposes a new framework to reconstruct a deformable soft object with complete geometry and consistent texture by introducing an incremental-completion self-generated template (SGT). By building a non-rigid registration that combines geometry and optical flow features, the SGT is dynamically updated and completed by supplementing the information from each initial state model. Then the updated SGT is reversely deformed to each state to obtain a sequence of dynamic reconstructed results with consistent geometry. Furthermore, a consistent Markov random field is also proposed to constrain mesh models in different states to generate consistent texture and guide non-rigid deformation. Experimental results show that our method achieves multi-state high-quality reconstruction effects, which provides a new solution for dynamically reconstructing colored soft objects.