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Rich heterogeneous data provided by social networks can be very big, which imposes considerable challenges for object extraction and image classification. Therefore, the objective of this work is to propose an unsupervised object cosegmentation method that could be notably efficient to improve image classification performance. The main goal of cosegmentation is to extract the salient common objects within each image. To this end, we propose to minimize an energy function based on the Markov Random Field using the saliency detection, while considering linear dependence of generated foreground histograms of the input image collection. In fact, the saliency detection is processed in two steps. In each image, we detect salient objects, by considering appearance similarity and spatial distributions of image pixels. Then, fuzzy quantification is used to correct the belonging of pixels to the foreground objects. Finally, an iterative optimization permits to enhance the final segmentation results. The proposed method has been validated as a preprocessing step for image classification. Indeed, to enhance cosegmentation-based classification performance, we have applied a semi-supervised object classification based on ensemble projection. Qualitative and quantitative evaluations of the proposed cosegmentation and classification techniques on the iCoseg, CDS and Oxford Flowers 17 datasets demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor image recognition robustness and poor model generalization in camouflaged object segmentation, inspired by the receptive field structure of the human visual system in neuroscience, this paper proposed a polarization imaging camouflage target detection method based on the receptive field network of focused attention. According to the polarization imaging target detection needs, it constructed the polarization imaging dataset that could effectively contain the background noise as well as obtain the detailed features of the target. The proposed method based on the recognition and localization network framework, which could effectively improve the discriminability and robustness of camouflaged target features by improving the feature extraction module and the decoder module, which exploited the relationship between the eccentricity and the size of the receptive field to cover multi-scale target information. The experimental validation carries out using a self- constructed dataset on multiple typical targets and comparing the subjective visual and objective evaluation metrics of the segmentation results with classical algorithms, and the results of the ablation experiments validate the effectiveness of the present segmentation method. [ABSTRACT FROM AUTHOR]

The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation. [ABSTRACT FROM AUTHOR]

On considering Human–Computer Interaction, the recognition of emotion over speech is developed from the niche phase into the most essential component. But, it is regarded as the most challenging issue due to the unclear features that are required to perform this task. In most of the conventional studies, various approaches have been carried out for retrieving the emotions from the signals for performing Speech Emotion Recognition that may involve diverse categorization and well-established speech analysis. Further, it has a better enhancement for upgrading human emotion analysis. This paper aims to build an intelligent recognition model using the improved heuristic algorithm with an adaptive deep learning approach. The foremost step is the collection of speech signals that are fetched from the standard data sources. It is then followed by the pre-processing phase, where the denoised signal is obtained. Then, the Short Term Fourier Transform is applied for generating the spectrogram image. Subsequently, the deep features are extracted through resultant image by influencing the method as an Attention-based Multi-scale Visual Geometry Group16 (AM-VGG16). Finally, the speech emotions are recognized by using the Adaptive Deep Markov Random Fields (ADMRF) with the help of attained deep features. Further, the parameter tuning is take place in the ADMRF by proposing the new algorithm as Modified Position in Squid Game Optimizer for increasing the recognition measures. Finally, the system is examined and computed with divergent measures. Hence, the accuracy of the proposed model attains 97.8 for dataset 1, 96.95 for dataset 2, 98.15 for dataset 3 and 96.65 for dataset 4. This highest value proves the system efficiency of differentiating the human emotions. Thus, the enhanced method demonstrates that the system attains extensive results in easily recognizing different emotions. [ABSTRACT FROM AUTHOR]

The accurate segmentation and classification of lung nodules using Computed Tomography (CT) scans play a significance part for early diagnosis of lung cancers. But, there are risks of misclassification that occurs due to incorrect suspiciousness of malignancy, alongside the reliance on the nodule size. In this research, the Categorical Hinge Loss Function based Convolutional Neural Network (CHLF-CNN) is proposed for multi-classification of lung nodule. This research comprises five main stages: Primarily, data collection is carried out through the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) as well as Lung Nodule Analysis 2016 (LUNA16) of CT-scan image for validating the model's effectiveness. Next, pre-processing is performed by deploying Median Filter based image denoising and Min-max normalization technique. Then, the pre-processed data is segmented by employing the Firefly Algorithm (FA) with the Cuckoo Search Algorithm (CSA) based Markov Random Field (MRF) approach. Then, feature extraction is carried out by utilizing the shape and texture-based features. Finally, classification is performed to classify the lung nodules into multi-classes. The CHLF-CNN attains a commendable accuracy of 96.54% and 99.56% respectively on the LIDC-IDRI and LUNA16 dataset, as opposed to the existing methods, Deep CNN and Morphology-based CNN (2PMorphCNN). [ABSTRACT FROM AUTHOR]

AbstractGaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods are often preferred due to the computational burden of the Bayesian approach. Over the last decade, Bayesian methods have seen substantial improvements, with some now capable of generating accurate estimates of graphs up to a thousand variables in mere minutes. Despite these advancements, a comprehensive review or empirical comparison of all recent methods has not been conducted. This paper delves into a wide spectrum of Bayesian approaches used for structure learning and evaluates their efficacy through a comprehensive simulation study. We also demonstrate how to apply Bayesian structure learning to a real-world data set and provide directions for future research. This study gives an exhaustive overview of this dynamic field for newcomers, practitioners, and experts. [ABSTRACT FROM AUTHOR]

The detection of harbors presents difficulties related to their diverse sizes, varying morphology and scattering, and complex backgrounds. To avoid the extraction of unstable geometric features, in this paper, we propose an unsupervised harbor detection method for polarimetric SAR images using context features and polarimetric reflection symmetry. First, the image is segmented into three region types, i.e., water low-scattering regions, strong-scattering urban regions, and other regions, based on a multi-region Markov random field (MRF) segmentation method. Second, by leveraging the fact that harbors are surrounded by water on one side and a large number of buildings on the other, the coastal narrow-band area is extracted from the low-scattering regions, and the harbor regions of interest (ROIs) are determined by extracting the strong-scattering regions from the narrow-band area. Finally, by using the scattering reflection asymmetry of harbor buildings, harbors are identified based on the global threshold segmentation of the horizontal, vertical, and circular co- and cross-polarization correlation powers of the extracted ROIs. The effectiveness of the proposed method was validated with experiments on RADARSAT-2 quad-polarization images of Zhanjiang, Fuzhou, Lingshui, and Dalian, China; San Francisco, USA; and Singapore. The proposed method had high detection rates and low false detection rates in the complex coastal environment scenarios studied, far outperforming the traditional spatial harbor detection method considered for comparison. [ABSTRACT FROM AUTHOR]

Markov chain geostatistics is a methodology for simulating categorical fields. Its fundamental model for conditional simulation is the Markov chain random field (MCRF) model, with the transiogram serving as its basic spatial correlation measure. There are different methods to obtain transiogram models for MCRF simulation based on sample data and expert knowledge: linear interpolation, mathematical model joint-fitting, and a mixed approach combining both. This study aims to explore the sensitivity of the MCRF model to different transiogram jointing modeling methods. Two case studies were conducted to examine how simulated results, including optimal prediction maps and simulated realization maps, vary with different sets of transiogram models. The results indicate that all three transiogram joint modeling methods are applicable, and the MCRF model exhibits a general insensitivity to transiogram models produced by different methods, particularly when sample data are sufficient to generate reliable experimental transiograms. The variations in overall simulation accuracies based on different sets of transiogram models are not significant. However, notable improvements in simulation accuracy for minor classes were observed when theoretical transiogram models (generated by mathematical model fitting with expert knowledge) were utilized. This study suggests that methods for deriving transiogram models from experimental transiograms perform well in conditional simulations of categorical soil variables when meaningful experimental transiograms can be estimated. Employing mathematical models for transiogram modeling of minor classes provides a way to incorporate expert knowledge and improve the simulation accuracy of minor classes. [ABSTRACT FROM AUTHOR]

Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly, to tackle the issues of foreground–background imbalance and scale variation, a module based on multi-dilated rate convolution fusion was integrated into a decoder. This module extended the receptive field through multi-dilated convolution, enhancing the model's capability to capture global features. Secondly, to address the diversity of scenes and background interference, a hybrid attention module based on large-kernel convolution was employed to improve the performance of the decoder. This module, based on a combination of spatial and channel attention mechanisms, enhanced the extraction of contextual information through large-kernel convolution. A convolution kernel selection mechanism was also introduced to dynamically select the convolution kernel of the appropriate receptive field, suppress irrelevant background information, and improve segmentation accuracy. Ablation studies on the Vaihingen and Potsdam datasets demonstrate that our decoder significantly outperforms the baseline in terms of mean intersection over union and mean F1 score, achieving an increase of up to 1.73% and 1.17%, respectively, compared with the baseline. In quantitative comparisons, the accuracy of our improved decoder also surpasses other algorithms in the majority of categories. The results of this paper indicate that our improved decoder achieves significant performance improvement compared with the old decoder in remote-sensing image-segmentation tasks, which verifies its application potential in the field of land-cover segmentation. [ABSTRACT FROM AUTHOR]

Atherosclerosis, a leading cause of cardiovascular disease, necessitates advanced and innovative modeling techniques to better understand and predict plaque dynamics. The present work presents two distinct hypothetical models inspired by different research fields: the logistic map from chaos theory and Markov models from stochastic processes. The logistic map effectively models the nonlinear progression and sudden changes in plaque stability, reflecting the chaotic nature of atherosclerotic events. In contrast, Markov models, including traditional Markov chains, spatial Markov models, and Markov random fields, provide a probabilistic framework to assess plaque stability and transitions. Spatial Markov models, visualized through heatmaps, highlight the spatial distribution of transition probabilities, emphasizing local interactions and dependencies. Markov random fields incorporate complex spatial interactions, inspired by advances in physics and computational biology, but present challenges in parameter estimation and computational complexity. While these hypothetical models offer promising insights, they require rigorous validation with real-world data to confirm their accuracy and applicability. This study underscores the importance of interdisciplinary approaches in developing theoretical models for atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

We introduce a model that can be used for the description of the distribution of species when there is scarcity of data, based on our previous work (Ballesteros et al. J Math Biol 85(4):31, 2022). We address challenges in modeling species that are seldom observed in nature, for example species included in The International Union for Conservation of Nature’s Red List of Threatened Species (IUCN 2023). We introduce a general method and test it using a case study of a near threatened species of amphibians called Plectrohyla Guatemalensis (see IUCN 2023) in a region of the UNESCO natural reserve “Tacaná Volcano”, in the border between Mexico and Guatemala. Since threatened species are difficult to find in nature, collected data can be extremely reduced. This produces a mathematical problem in the sense that the usual modeling in terms of Markov random fields representing individuals associated to locations in a grid generates artificial clusters around the observations, which are unreasonable. We propose a different approach in which our random variables describe yearly averages of expectation values of the number of individuals instead of individuals (and they take values on a compact interval). Our approach takes advantage of intuitive insights from environmental properties: in nature individuals are attracted or repulsed by specific features (Ballesteros et al. J Math Biol 85(4):31, 2022). Drawing inspiration from quantum mechanics, we incorporate quantum Hamiltonians into classical statistical mechanics (i.e. Gibbs measures or Markov random fields). The equilibrium between spreading and attractive/repulsive forces governs the behavior of the species, expressed through a global control problem involving an energy operator. [ABSTRACT FROM AUTHOR]

An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods. [ABSTRACT FROM AUTHOR]

Currently, many real-time semantic segmentation networks aim for heightened accuracy, inevitably leading to increased computational complexity and reduced inference speed. Therefore, striking a balance between accuracy and speed has emerged as a crucial concern in this domain. To address these challenges, this study proposes a dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation (MAFNet). The first key component, the semantics guide spatial-details module (SGSDM) not only facilitates precise boundary extraction and fine-grained classification, but also provides semantic-based feature representation, thereby enhancing support for spatial analysis and decision boundaries. The second component, the multiscale atrous pyramid pooling module (MSAPPM), is designed by combining dilation convolution with feature pyramid pooling operations at various dilation rates. This design not only expands the receptive field, but also aggregates rich contextual information more effectively. To further improve the fusion of feature information generated by the dual-branch, a bilateral fusion module (BFM) is introduced. This module employs cross-fusion by calculating weights generated by the dual-branch to balance the weight relationship between the dual branches, thereby achieving effective feature information fusion. To validate the effectiveness of the proposed network, experiments are conducted on a single A100 GPU. MAFNet achieves a mean intersection over union (mIoU) of 77.4% at 70.9 FPS on the Cityscapes test dataset and 77.6% mIoU at 192.5 FPS on the CamVid test dataset. The experimental results conclusively demonstrated that MAFNet effectively strikes a balance between accuracy and speed. [ABSTRACT FROM AUTHOR]

Data clustering has attracted the interest of scholars in many fields. In recent years, using heuristic algorithms to solve data clustering problems has gradually become a tendency. The black hole algorithm (BHA) is one of the popular heuristic algorithms among researchers because of its simplicity and effectiveness. In this paper, an improved self-adaptive logarithmic spiral path black hole algorithm (SLBHA) is proposed. SLBHA innovatively introduces a logarithmic spiral path and random vector path to BHA. At the same time, a parameter is used to control the randomness, which enhances the local exploitation ability of the algorithm. Besides, SLBHA designs a replacement mechanism to improve the global exploration ability. Finally, a self-adaptive parameter is introduced to control the replacement mechanism and maintain the balance between exploration and exploitation of the algorithm. To verify the effectiveness of the proposed algorithm, comparison experiments are conducted on 13 datasets creatively using the evaluation criteria including the Jaccard coefficient as well as the Folkes and Mallows index. The proposed methods are compared with the selected algorithms such as the whale optimization algorithm (WOA), compound intensified exploration firefly algorithm (CIEFA), improved black hole algorithm (IBH), etc. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on both external criteria and quantization error of the clustering problem. [ABSTRACT FROM AUTHOR]

Currently, the medical field is witnessing an increase in the use of machine learning techniques. Supervised learning methods adopted in classification, prediction, and segmentation tasks for medical images always experience decreased performance when the training and testing datasets do not follow the independent and identically distributed assumption. These distribution shift situations seriously influence machine learning applications' robustness, fairness, and trustworthiness in the medical domain. Hence, in this article, we adopt the CycleGAN (generative adversarial network) method to cycle train the computed tomography data from different scanners/manufacturers. It aims to eliminate the distribution shift from diverse data terminals based on our previous work [14]. However, due to the model collapse problem and generative mechanisms of the GAN-based model, the images we generated contained serious artifacts. To remove the boundary marks and artifacts, we adopt score-based diffusion generative models to refine the images voxel-wisely. This innovative combination of two generative models enhances the quality of data providers while maintaining significant features. Meanwhile, we use five paired patients' medical images to deal with the evaluation experiments with structural similarity index measure metrics and the segmentation model's performance comparison. We conclude that CycleGAN can be utilized as an efficient data augmentation technique rather than a distribution-shift-eliminating method. In contrast, the denoising diffusion the denoising diffusion model is more suitable for dealing with the distribution shift problem aroused by the different terminal modules. The limitation of generative methods applied in medical images is the difficulty in obtaining large and diverse datasets that accurately capture the complexity of biological structure and variability. In our following research, we plan to assess the initial and generated datasets to explore more possibilities to overcome the above limitation. We will also incorporate the generative methods into the federated learning architecture, which can maintain their advantages and resolve the distribution shift issue on a larger scale. [ABSTRACT FROM AUTHOR]

Rotary drilling rigs are extensively used in foundational construction, with main components including the mast, drill rods, drill bits, power head, and cockpit. The mast, a critical component, must be of high quality to ensure operational reliability. To ensure the reliability of the mast during operation, it is necessary to detect defects in the most stress-prone areas. Based on finite element analysis and practical engineering experience, the welds on the large circular disc of the drill mast are identified as the most vulnerable to damage. Accordingly, this paper prepared test specimens specifically from these weld locations and conducted X-ray inspections, obtaining images for analysis. Given the inevitable presence of noise due to objective conditions, the images first underwent median filtering. For effective subsequent defect detection, edge detection was performed on the images. Among various edge detection operators, the Scharr operator was found to be highly effective. To further enhance the edge detection performance, this paper introduces an improved Scharr edge detection algorithm that incorporates dynamic thresholds and Markov Random Field (MRF) techniques. The enhanced algorithm was evaluated using signal-to-noise ratio and edge localization accuracy as metrics, and it was found to significantly improve performance. [ABSTRACT FROM AUTHOR]

Considering the improved detection of biological analytes for affinity analysis is highly desirable, a metasurface plasmon resonance (Meta‐SPR)‐based imaging system, incorporating a localized SPR sensing platform with different microfluidic systems and employing simple bright‐field imaging, is established in this study. This system enables low‐level analyte concentration analysis, ranging from 100 pm to 100 nm, with the real‐time removal of nonspecific binding signals within the same device field of view. Combined with microfluidic systems and microdroplet spotting, it is possible to automatically measure the kinetic curves of a sample at ten concentration gradients or detect the specific responses of multiple samples in a single experiment simultaneously. This system can inexpensively and conveniently achieve complex detection functions, demonstrating an innovative breakthrough in sensor detection. [ABSTRACT FROM AUTHOR]

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer's channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management. [ABSTRACT FROM AUTHOR]

To address the issue of needing drone aerial photogrammetry in certain projects where there is a lack of basic control points around the survey area, or it is difficult for personnel to enter the area to set up photo control points, this paper proposed a fast aerial survey method for drones without photo control based on global gravity field models, and the accuracy of this method was verified through experiments. The research used three global gravity field models: EGM2008, XGM2019, and EIGEN-6C4, to calculate the height anomalies in six experimental areas and converted the geodetic heights in the image exterior orientation elements to normal heights, which were then input into the aerial survey software for aerial triangulation and 3D modeling. The analysis of the results shows that, the elevation accuracy of the photo control-free aerial survey results based on the EGM2008, XGM2019, and EIGEN-6C4 models is between 10 ~ 20 cm, which meets the requirements of 1:2 000 mapping precision. To further improve the accuracy of the results, this paper adopted a method based on the EIGEN-6C4 gravity field model and the "remove-fit-restore" normal height calculation method, which converted the geodetic heights in the exterior orientation elements of the images obtained by the drone aerial survey system into normal heights. The method calculated the height anomalies of 16 GNSS leveling points across the country, fitted a normal height model for the experimental area, and achieved higher accuracy drone aerial surveys without photo control. The experimental results show that, the elevation accuracy of the photo control-free aerial survey results based on the EIGEN-6C4 model "remove-fit-restore"method can reach the centimeter level, which is higher than the method based only on the EGM2008, XGM2019, and EIGEN-6C4 gravity models, and can meet the requirements of 1:500 mapping precision. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered areas in water maps. However, these methods are not tailored for and validated in cloudy and rainy flooding scenarios with rapid water extent changes and limited clear-sky observations, leaving room for further improvements. This study investigated and developed a novel reconstruction method for time series flood extent mapping, supporting spatially seamless monitoring of flood extents. The proposed method first identified surface water from time series images using a fine-tuned large foundation model. Then, the cloud-covered areas in the water maps were reconstructed, adhering to the introduced submaximal stability assumption, on the basis of the prior water occurrence data in the Global Surface Water dataset. The reconstructed time series water maps were refined through spatiotemporal Markov random field modeling for the final delineation of flooding areas. The effectiveness of the proposed method was evaluated with Harmonized Landsat and Sentinel-2 datasets under varying cloud cover conditions, enabling seamless flood mapping at 2–3-day frequency and 30 m resolution. Experiments at four global sites confirmed the superiority of the proposed method. It achieved higher reconstruction accuracy with average F1-scores of 0.931 during floods and 0.903 before/after floods, outperforming the typical gap-filling method with average F1-scores of 0.871 and 0.772, respectively. Additionally, the maximum flood extent maps and flood duration maps, which were composed on the basis of the reconstructed water maps, were more accurate than those using the original cloud-contaminated water maps. The benefits of synthetic aperture radar images (e.g., Sentinel-1) for enhancing flood mapping under cloud cover conditions were also discussed. The method proposed in this paper provided an effective way for flood monitoring in cloudy and rainy scenarios, supporting emergency response and disaster management. The code and datasets used in this study have been made available online (https://github.com/dr-lizhiwei/SeamlessFloodMapper). [ABSTRACT FROM AUTHOR]

We consider an ensemble Kalman inversion scheme for inverse elastic scattering problems in which the unknown quantity is the shape of the scatterer. Assume that the scatterer is a piecewise constant function with known value inside inhomogeneities. The level set method is described as an implicit representation of the scatterer boundary, with Gaussian random fields serving as prior to provide information on the level set functions. The ensemble Kalman filter method is then employed based on the level set functions to reconstruct the shape of the scatterer. We demonstrate the effectiveness of the proposed method using several numerical examples. [ABSTRACT FROM AUTHOR]

At present, footprint image retrieval based on deep learning mainly focuses on complete footprint, but many of the footprints obtained in the field of public safety and criminal investigation are incomplete forms, Therefore, the feature analysis of incomplete footprint has important practical significance. Based on multiple scale features fusion, we proposed a method to solve incomplete footprints retrieval. On the basis of extracting the global feature of footprint, this method simultaneously extracts multiple local features from different stages of the backbone network to supplement the footprint feature information. The multi-scale feature orthogonal fusion module is used to reduce redundant features, improve the expression ability of footprint features, and solve the problem of incomplete footprint retrieval to a certain extent. The experiment shows that our method has certain effectiveness in retrieving problems on incomplete footprint, with a Top 1 accuracy of 87.08%, expanding the scope of footprint research. [ABSTRACT FROM AUTHOR]

We study the dynamic orbit about Phobos of the Martian Moon eXploration (MMX) spacecraft and simulate gravity field estimation using Doppler, image landmarks, and LIght Detection And Ranging (LIDAR) data based on a mission plan of MMX and investigate whether the differences in the internal density structure of Phobos can be detected through this mission. Degree 2 Stokes coefficients of the Phobos gravity field, C ¯ 20 and C ¯ 22 , which are necessary to obtain the moment of inertia by combining with the libration amplitude, could be determined with an accuracy within the order of 0.1% using datasets from two-dimensional (2D) quasi-satellite orbits (QSOs). If observations from 3D-QSO could be realized at low altitude, coefficients up to degree and order 5 could be estimated, which could be used to detect regional density anomalies. Moreover, the observation data from the ascending trajectory after landing on the Phobos surface can be used to detect local density anomalies around the landing site, which could help interpret the origin of the samples and understand the internal structure of Phobos. [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world. [ABSTRACT FROM AUTHOR]

Urban planning has, in recent years, been significantly assisted by remote sensing data. The data and techniques that are used are very diverse and are available to government agencies as well as to private companies that are involved in planning urban and peri-urban areas. Synthetic aperture radar data are particularly important since they provide information on the geometric and electrical characteristics of ground objects and, at the same time, are unaffected by sunlight (day–night) and cloud cover. SAR data are usually combined with optical data (fusion) in order to increase the reliability of the terrain information. Most of the existing relative classification methods have been reviewed. New techniques that have been developed use decorrelation and interferometry to record changes on the Earth's surface. Texture-based features, such as Markov random fields and co-occurrence matrices, are employed, among others, for terrain classification. Furthermore, target geometrical features are used for the same purpose. Among the innovative works presented in this manuscript are those dealing with tomographic SAR imaging for creating digital elevation models in urban areas. Finally, tomographic techniques and digital elevation models can render three-dimensional representations for a much better understanding of the urban region. The above-mentioned sources of information are integrated into geographic information systems, making them more intelligent. In this work, most of the previous techniques and methods are reviewed, and selected papers are highlighted in order for the reader-researcher to have a complete picture of the use of SAR in urban planning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The bearing capacity of shallow foundation is significantly affected by stratum uncertainty, mainly including geological uncertainty and spatial variability of soil properties. This paper aims to develop a general framework of shallow foundation to characterize the influence of two types of uncertainties on bearing capacity quantitatively. The geological uncertainty is simulated by Markov random field with different borehole schemes. The spatial variability is characterized using random field theory considering variations of the vertical scale of fluctuation. The bearing capacity of shallow foundation analysis is then conducted based on the proposed calculation framework. Borehole data and soil properties collected from Shenzhen Mawan are adopted as an example to illustrate the proposed framework. A reduction factor is developed to reveal the relationship of two category uncertainties on bearing capacity. A contribution rate indicator is defined to quantitatively evaluate the impact of two category uncertainties. Results show that the geological uncertainty greatly influences the calculation results when boreholes are sparse. However, spatial variability dominates in two kinds of uncertainties when having relatively more boreholes. [ABSTRACT FROM AUTHOR]

Multivariate extreme value distributions are a common choice for modeling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models is challenging. We propose to combine bivariate max‐stable distributions into a Markov random field with respect to a tree. Although in general not max‐stable itself, this Markov tree is attracted by a multivariate max‐stable distribution. The latter serves as a tree‐based approximation to an unknown max‐stable distribution with the given bivariate distributions as margins. Given data, we learn an appropriate tree structure by Prim's algorithm with estimated pairwise upper tail dependence coefficients as edge weights. The distributions of pairs of connected variables can be fitted in various ways. The resulting tree‐structured max‐stable distribution allows for inference on rare event probabilities, as illustrated on river discharge data from the upper Danube basin. [ABSTRACT FROM AUTHOR]

Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based on either the Dirichlet process or on more flexible generalizations, such as the normalized random measures with independent increments (NRMI). The literature on finite-dimensional discrete priors is much more limited and mostly confined to the standard Dirichlet-multinomial model. While such a specification may be attractive due to conjugacy, it suffers from considerable limitations when it comes to addressing clustering problems. In order to overcome these, we introduce a novel class of priors that arise as the hierarchical compositions of finite-dimensional random discrete structures. Despite the analytical hurdles such a construction entails, we are able to characterize the induced random partition and determine explicit expressions of the associated urn scheme and of the posterior distribution. A detailed comparison with (infinite-dimensional) NRMIs is also provided: indeed, informative bounds for the discrepancy between the partition laws are obtained. Finally, the performance of our proposal over existing methods is assessed on a real application where we study a publicly available dataset from the Italian education system comprising the scores of a mandatory nationwide test. [ABSTRACT FROM AUTHOR]

We consider the clustering of extremes for stationary regularly varying random fields over arbitrary growing index sets. We study sufficient assumptions on the index set such that the limit of the point processes of the exceedances above a high threshold exists. Under the so-called anti-clustering condition, the extremal dependence is only local. Thus the index set can have a general form compared to previous literature (Basrak and Planinić in Bernoulli 27(2):1371–1408, 2021; Stehr and Rønn-Nielsen in Extremes 24(4):753–795, 2021). However, we cannot describe the clustering of extreme values in terms of the usual spectral tail measure (Wu and Samorodnitsky in Stochastic Process Appl 130(7):4470–4492, 2020) except for hyperrectangles or index sets in the lattice case. Using the recent extension of the spectral measure for star-shaped equipped space (Segers et al. in Extremes 20:539–566, 2017), the Υ -spectral tail measure provides a natural extension that describes the clustering effect in full generality. [ABSTRACT FROM AUTHOR]

The simple and efficient network structure of U-Net is widely used in medical image segmentation, and many scholars have made various researches on the U-Net structure. This paper elucidates in the following: firstly, the paper summarizes the key challenges of the U-Net network in the field of medical image segmentation; next, it elaborates the formats and characteristics of medical image datasets that are commonly used in the U-Net network; then, it summarizes the six improvement mechanism of U-Net: skip connection mechanism, generative adversarial network, residual connection mechanism, 3D-UNet, Transformer mechanism, and dense connecting mechanism. Finally, the paper discusses the relationship between these improvement mechanisms and commonly used medical data formats, and points out the ideas and directions for future improvement, so as to stimulate the unlimited potential of U-Net in medical image segmentation. [ABSTRACT FROM AUTHOR]

In response to the limitations of existing methods in differentiating between vegetables and all types of weeds in farmlands, a new image segmentation method is proposed based on the improved YOLOv7-tiny. Building on the original YOLOv7-tiny framework, we replace the CIoU loss function with the WIoU loss function, substitute the Leaky ReLU loss function with the SiLU activation function, introduce the SimAM attention mechanism in the neck network, and integrate the PConv convolution module into the backbone network. The improved YOLOv7-tiny is used for vegetable target detection, while the ExG index, in combination with the OTSU method, is utilized to obtain a foreground image that includes both vegetables and weeds. By integrating the vegetable detection results with the foreground image, a vegetable distribution map is generated. Subsequently, by excluding the vegetable targets from the foreground image using the vegetable distribution map, a single weed target is obtained, thereby achieving accurate segmentation between vegetables and weeds. The experimental results show that the improved YOLOv7-tiny achieves an average precision of 96.5% for vegetable detection, with a frame rate of 89.3 fps, Params of 8.2 M, and FLOPs of 10.9 G, surpassing the original YOLOv7-tiny in both detection accuracy and speed. The image segmentation algorithm achieves a mIoU of 84.8% and an mPA of 97.8%. This method can effectively segment vegetables and a variety of weeds, reduce the complexity of segmentation with good feasibility, and provide a reference for the development of intelligent plant protection robots. [ABSTRACT FROM AUTHOR]

Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in terms of their parents according to a directed acyclic graph (DAG). We focus on max-linear graphical models with respect to a special type of graph, which we call a tree of transitive tournaments. The latter is a block graph combining in a tree-like structure a finite number of transitive tournaments, each of which is a DAG in which every two nodes are connected. We study the limit of the joint tails of the max-linear model conditionally on the event that a given variable exceeds a high threshold. Under a suitable condition, the limiting distribution involves the factorization into independent increments along the shortest trail between two variables, thereby imitating the behaviour of a Markov random field. We are also interested in the identifiability of the model parameters in the case when some variables are latent and only a subvector is observed. It turns out that the parameters are identifiable under a criterion on the nodes carrying the latent variables which is easy and quick to check. [ABSTRACT FROM AUTHOR]

In practical Electromyography (EMG) control, it is necessary to select appropriate number of surface EMG channels with ideal classification performance. In this paper, the contribution of different muscles to motor tasks is taken as the optimization criterion for optimal channel selection, and a channel selection method based on muscle synergy(MS) is proposed. Firstly, the raw EMG signal is preprocessed to extract EMG features, and then non-negative matrix factorization (NMF) algorithm is used to extract the muscle synergy matrix for each gesture and converted. Secondly, the muscle weight coefficients of each gesture on each EMG channel are summed to obtain the importance coefficients of all EMG channels. Finally, the classification is carried out by support vector machine(SVM), random forest(RF) and K-nearest neighbor(KNN) classifier. The method is tested by using surface EMG recorded in DB5 subdatabase of Ninapro database. The test results show that when extracting 10 optimal channels, compared with the sequential forward selection(SFS), Markov random field(MRF) and Relief-F channel selection methods proposed in previous studies, the recognition accuracy of the EMG subsets determined by this method is similar to that obtained by MRF and Relief-F method, and is slightly lower than that obtained by SFS method, but the computational cost is lower than that of SFS, MRF and Relief-F methods. [ABSTRACT FROM AUTHOR]

Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Polyp detection and localization are essential tasks for colonoscopy. U‐shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long‐range dependencies modeling limits their receptive fields. Purpose: Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long‐range dependencies modeling. Methods: A novel architecture combining with multi‐scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U‐shape net to learn explicit global context and long‐range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multiple resolution. Results: Four public datasets and one in‐house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir‐SEG and CVC‐ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state‐of‐the‐art methods. Conclusions: The proposed model produced more accurate polyp segmentation than current methods on four different public and one in‐house datasets. Its capability of polyps segmentation in different sizes shows the potential clinical application [ABSTRACT FROM AUTHOR]

Background: Segmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. Methods: We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. Results: In experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. Conclusion: Sophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data. [ABSTRACT FROM AUTHOR]

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query's foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL- 5 i and COCO- 20 i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. [ABSTRACT FROM AUTHOR]

The current mainstream image semantic segmentation networks often suffer from mis-segmentation, segmentation discontinuity, and high model complexity, which limit their application in real-time processing scenarios. The work established a lightweight neural network model for semantic segmentation to address this issue. The network used a dual-branch strategy to solve low semantic boundary segmentation accuracy in semantic segmentation tasks. The semantic branch applied the characteristics of the deeplabv3 + model structure. Besides, dilated convolutions with different dilation rates in the encoder were used to expand the receptive field of convolutional operations and enhance the ability to capture local features. The boundary refinement branch extracted second-order differential features of the input image through the Laplace operator, and it gradually refined the second-order differential features through a feature refinement extraction module to obtain advanced semantic features. A convolutional block attention module was introduced to filter the features from both the channel and spatial dimensions and finally fused with the semantic branch to achieve constrained segmentation boundary effects. Based on this, a multi-channel attention fusion module was proposed to aggregate features from different stages. Low-resolution features were first up-sampled and then fused with high-resolution features to enhance the spatial information of high-level features. Proposed network's effectiveness was demonstrated through extensive experiments on the MaSTr1325 dataset, the MID dataset, the Camvid dataset, and the PASCAL VOC2012 dataset, with mIoU of 98.1, 73.1, and 81.1% and speeds of 111.40, 100.36, and 111.43 fps on a single NVIDIA RTX 3070 GPU, respectively. [ABSTRACT FROM AUTHOR]

Lung cancer is a kind of harmful cancer type that originates from the lungs. In this research, the lung lobe segmentation is carried out using Markov Random Field (MRF)-based Artificial Hummingbird Cuckoo algorithm (AHCA). The AHCA algorithm is modelled by considering the benefits of both the Artificial Hummingbird algorithm (AHA) and the Cuckoo search (CS) algorithm. Moreover, the lung cancer classification is done with ShuffleNet, which is trained by the Artificial Hummingbird Firefly optimization algorithm (AHFO) which is the integration of AHA and Firefly algorithm (FA). In this research, two algorithms are devised for both segmentation and classification. From these two algorithms, the AHA algorithm is used for updating the location. The AHA algorithm had three phases, such as foraging, guided foraging and migrating foraging where the guided foraging stage is selected to update the location for both segmentation and classification. Besides, the developed AHFO-based ShuffleNet scheme attained superior performance with respect to the testing accuracy of 0.9071, sensitivity of 0.9137 and specificity of 0.9039. The performance improvement of the proposed method for testing accuracy is 6.615%, 3.197%, 2.756%, and 1.764% higher than the existing methods. In future, the performance will be boosted by the advanced scheme for identifying the grade of disease. [ABSTRACT FROM AUTHOR]

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model's generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images. [ABSTRACT FROM AUTHOR]

Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks. [ABSTRACT FROM AUTHOR]

Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract features. To address this issue, we propose SSGNet based on UNet, which comprises a selective multi-scale receptive field (SMRF) module, a selective kernel self-attention (SKSA) module, and a skip connection attention module (SCAM). The SMRF and SKSA modules have the same function but work in different modality groups. SMRF functions in the T1 and T1ce modality groups, while SKSA is implemented in the T2 and FLAIR modality groups. Their main tasks are to reduce the image size by half, further extract fused features within the groups, and prevent information loss during downsampling. The SCAM uses high-level features to guide the selection of low-level features in skip connections. To improve performance, SSGNet also utilizes deep supervision. Multiple experiments were conducted to evaluate the effectiveness of our model on the BraTS2018 dataset. SSGNet achieved Dice coefficient scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) of 91.04, 86.64, and 81.11, respectively. The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks. [ABSTRACT FROM AUTHOR]

In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state‐of‐the‐art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP‐hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors' approach on the D‐Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed Deep Adaptive Graph Clustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρk which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., (H|ρ) and (ρ|H), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets. [ABSTRACT FROM AUTHOR]

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9. [ABSTRACT FROM AUTHOR]

For early detection of cancer tumors, the semantic segmentation based technique is proposed because the existing numerous methods fail while classifying due to accuracy and ineffectual decision-making. Therefore, in this paper, the hybrid semantic segmentation networks are introduced. In the beginning, the input image sets are applied into the pre-processing phase and after that, subject to the process of segmentation. The pre-processing process of image contrast enhancement is done by Adaptive Local Gamma Correction (ALGC). The semantic segmentation topology is done by using the hybrid network of the DenseNet-121 model with Attention based pyramid scene parsing network (Att-PSPnet). The feature map extraction and scene parsing are handled by the Att-PSPnet network. The Attention Gate mechanism is introduced to improve the quality of the high-dimensional hidden layer features by highlighting the useful information, and unnecessary information and noise are suppressed. To make the efficient decision and enhance prediction accuracy, the pyramid dilated convolution module (PDM) is a branch of the attention-based pyramid pooling module that enlarges the receptive field to extract global information. Additionally, the global average pooling (GAP) layer is introduced at the output of the feature map. The performance of the proposed method is validated using the Google Colab environment with a histologically confirmed dataset. The experimental results are compared with existing methods like FCN, Unet, and PSPNet in terms of IoU, accuracy, precision, recall, F1 score, IoU, and more. The proposed method achieves 94.68% prediction accuracy which is higher than the existing approaches. [ABSTRACT FROM AUTHOR]

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph's global structure. Consequently, this enhances the model's precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral–Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral–Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model's potential in improving brain tumor segmentation accuracy, precision, and computational efficiency. [ABSTRACT FROM AUTHOR]