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Positron emission tomography (PET) is a widely utilized medical imaging modality that uses positron-emitting radiotracers to visualize biochemical processes in a living body. The spatiotemporal distribution of a radiotracer is estimated by detecting the coincidence photon pairs generated through positron annihilations. In human tissue, about 40% of the positrons form positroniums prior to the annihilation. The lifetime of these positroniums is influenced by the microenvironment in the tissue and could provide valuable information for better understanding of disease progression and treatment response. Currently, there are few methods available for reconstructing high-resolution lifetime images in practical applications. This paper presents an efficient statistical image reconstruction method for positronium lifetime imaging (PLI). We also analyze the random triple-coincidence events in PLI and propose a correction method for random events, which is essential for real applications. Both simulation and experimental studies demonstrate that the proposed method can produce lifetime images with high numerical accuracy, low variance, and resolution comparable to that of the activity images generated by a PET scanner with currently available time-of-flight resolution.

This article, for the first time, investigates the potential of Sludge Gasification Slag (SGS), a byproduct of urban sewage sludge gasification, as a lightweight aggregate in ultra-high-performance concrete (UHPC), proposing a novel sustainable solution for the utilization of SGS. The UHPC mix design followed the modified Andreasen and Andersen model, incorporating pretreated SGS, cement, silica fume (SF), river sand, and a high-efficiency water-reducing agent. A total of eight experimental groups were developed, including five pre-wetted groups (I1–I5) and three dry groups (N1–N3), to evaluate the rheological and mechanical properties of UHPC. For the first time, this study combines scanning electron microscopy (SEM) and nitrogen adsorption techniques to investigate the interfacial transition zone (ITZ) and porosity of SGS-UHPC, providing insights into the influence of SGS on the matrix. The results show that SGS, due to its irregular particle shape and high water absorption capacity, negatively impacts the flowability of the fresh mix. However, when the SGS content reached 7.5%, the plastic viscosity of the UHPC mix peaked. Notably, after 28 days of curing, the compressive strength of the 5% pre-wetted SGS group exceeded that of the control group by 5%, indicating a time-dependent strength improvement. This enhancement is primarily attributed to the water release effect of SGS, which optimizes the ITZ and strengthens the overall matrix. The findings suggest that SGS, when used at dosages below 7.5%, can be effectively incorporated into UHPC, offering a promising, environmentally friendly alternative for sustainable construction applications. [ABSTRACT FROM AUTHOR]

Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes and the linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fully connected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks. [ABSTRACT FROM AUTHOR]

BackgroundParaspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.MethodsTo facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.ResultsWe found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p

Abstract In the context of civil engineering applications, vibration responses are complex, exhibiting variations in time and space and often containing nonlinearity and uncertainties not considered during data collection. These responses can also be contaminated by various sources, impacting damage identification processes. A significant challenge is how to effectively remove noise from these data to obtain reliable damage indicators that are unresponsive to noise and environmental factors.This study proposes a new denoising algorithm based on discrete wavelet transform (DWT) that addresses this issue. The suggested method offers a strategy for denoising using distinct thresholds for positive and negative coefficient values at each band and applying denoising process to both detail and trend components. The results prove the effectiveness of the technique and show that Bayes thresholding performs better than the other techniques in terms of the evaluated metrics. This suggests that Bayes thresholding is a more accurate and robust technique for thresholding compared to other common techniques.

Archaeological pottery X-radiography is mainly used for two applications: fabric characterization and identification of forming techniques. Both applications require imaging of tempering materials and other additives. With digital X-radiography, it is easy to enhance the image to compute and characterize these materials. In this study, a combination of ImageJ plug-ins such as “threshold”, “analyze particles”, and “fit polynomial” were used to describe tempering materials of a set composed of archaeological pottery sherds. It was found that two different types of tempering materials were used. The first type was characterized by a grain size of less than 0.5 mm and no well-formed particles. In contrast, the second group had a grain size larger than 0.5 mm and well-formed particles.

Denoising is an important step in seismic data processing that can improve the results of other processing steps and, consequently, the interpretation of seismic sections. Both land and marine seismic data contain coherent and incoherent noise. Swell noise is one of the noises present in marine seismic data. It has a high amplitude and low frequency band and is observed as vertical bands in marine seismic data. Developing methods that can attenuate swell noise more while causing the least damage to the signal in marine seismic data seems necessary. There are various methods for attenuating swell noise, each with its advantages and limitations. The approach of most denoising methods is to maximize the separation of noise from the signal. Variational mode decomposition (VMD) has shown promising results in seismic denoising by separating different modes of signal and noise. The goal is to obtain a set of intrinsic mode functions (IMFs) and their corresponding center frequencies. The main approach of VMD is to decompose the input signal into a number of sub-signals (modes), which also have sparsity properties while recovering the input signal. Here, the sparsity of each mode is chosen as the bandwidth of that mode. In other words, it is assumed that each mode is more concentrated around the center frequency, which is determined in the decomposition. Another important issue in noise attenuation is that the denoising method should be able to perform the denoising process automatically with minimal user intervention. The proposed denoising algorithm in this paper consists of four steps: decomposition, identification, filtering, and reconstruction. In the first step, the seismic signal is decomposed into its constituent modes using the VMD method. In the second step, the modes contaminated with noise is identified according to the autocorrelation function of the modes, where the higher values of standard deviation of autocorrelation above a predefined measure present noisy modes. In the third step, the noise is removed through a filtering process based on a hard thresholding procedure. In the final step, the constituent modes, including the clean untouched modes, the denoised modes, and the residue, are summed together and the signal is denoised. The proposed algorithm's efficacy is demonstrated through its application to both synthetic and real seismic data. Notably, the method demonstrates superior performance compared to conventional high-pass filtering and time-frquency denoising (TFDN) method, effectively attenuating swell noise while preserving valuable low-frequency seismic information. [ABSTRACT FROM AUTHOR]

Archaeological pottery X-radiography is mainly used for two applications: fabric characterization and identification of forming techniques. Both applications require imaging of tempering materials and other additives. With digital X-radiography, it is easy to enhance the image to compute and characterize these materials. In this study, a combination of ImageJ plug-ins such as "threshold", "analyze particles", and "fit polynomial" were used to describe tempering materials of a set composed of archaeological pottery sherds. It was found that two different types of tempering materials were used. The first type was characterized by a grain size of less than 0.5 mm and no well-formed particles. In contrast, the second group had a grain size larger than 0.5 mm and well-formed particles. [ABSTRACT FROM AUTHOR]

Identification of human action is a crucial field in machine learning with applications in healthcare monitoring and smart home automation. Recognizing and detecting human actions in videos is essential for various real-world applications. This paper presents a comparative study of Support Vector Machine (SVM), Thresholding algorithm, and MobileNetV2 for human activity recognition. These models are evaluated for accuracy, computational efficiency, and suitability for real-time applications. The study addresses the challenge of detecting humans in video sequences from a thermal camera in low light conditions, dealing with complexities like illumination changes, motion blur, and varying perspectives. Experimental results demonstrate that while MobileNetV2 outperforms SVM in accuracy and real-time processing capabilities, SVM offers a simpler and less resourceintensive solution. Deep pre-trained Convolutional Neural Networks (CNNs) like MobileNetV2 were used to extract informative features from detected human patches. The performance evaluation focused on four human movements: walking, running, duck walking, and crawling. Experimental data showed that MobileNetV2 achieved an average accuracy of 92.9%, maintaining high accuracy even in challenging conditions such as blurring, Gaussian noise, and low light. [ABSTRACT FROM AUTHOR]

A physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes. [ABSTRACT FROM AUTHOR]

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Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy is highly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR , the first data-analysis procedure that automates the assessment of any linear partition classifier's partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR 's robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR 's use. [ABSTRACT FROM AUTHOR]

In this article, we derive the minimax detection boundary for testing a sub-block of variables in a precision matrix under the Gaussian distribution. Compared to the results on the minimum rate of signals for testing precision matrices in literature, our result gives the exact minimum signal strength in a precision matrix that can be detected. We propose a thresholding test that is able to achieve the minimax detection boundary under certain cases by adaptively choosing the threshold level. The asymptotic distribution of the thresholding statistic for precision matrices is derived. Power analysis is conducted to show the proposed test is powerful against sparse and weak signals, which cannot be detected by the existing L m a x and L 2 tests. Simulation studies show the proposed test has an accurate size around the nominal level and is more powerful than the existing tests for detecting sparse and weak signals in precision matrices. Real data analysis on brain imaging data is carried out to illustrate the utility of the proposed test in practice, which reveals functional connectivity between brain regions for Alzheimer's disease patients and normal healthy people. [ABSTRACT FROM AUTHOR]

Mathematical foundations and algorithms for recognizing bichromatic two-dimensional graphic codes, regardless of their type (QR codes, DataMatrix, GridMatrix, etc.) are presented. The stages of achieving the result include detecting the code, localizing it within an arbitrary quadrilateral, transforming the quadrilateral to a canonical square, constructing a grid of elements (modules) of the square code, and filling it with a sequence of bits. It is shown that perspective transformation formulas make it possible to transform localized quadrangular regions to canonical squares with an acceptable error level for further processing. A flat grid of square code elements is formed based on the search for extrema of the derivatives of the pixel intensity distribution of the square image along the axes x and y. The algorithm for filling grid cells (code modules) with a sequence of zeros and ones uses information about the average intensity of each such cell. At the end of the paper, the algorithms are tested on a variety of real images of two-dimensional codes, and the limitations of the proposed algorithms are examined. [ABSTRACT FROM AUTHOR]

Abstract Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches—scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common ‘crackling noise’ approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Abstract Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority.

Canopy cover fraction is one of the most important criteria for investigating the crop growth and yield and is one of the input data of most plant models. Canopy cover fraction is an easier measurement than the other methods which id depended on field observations or image processing beyond the visible spectrum. In this study, drone images of the sugar beet field in the cropping season of 2015-2016 and on the four dates from late May to late June at the Lindau center of plant sciences research, Switzerland were used. The research was conducted by six plant discrimination indices and three distinct thresholding algorithms to segment sugar beet vegetation. Then, among the 18 investigated methods, the best 6 methods were evaluated by comparing their values with the ground truth values in 30 different regions of the farm and on four dates from the beginning of the four-leaf stage to the end of the six-leaf stage. Results showed that the ExG, GLI, and RGBVI indices, in combination with the Otsu and RidlerCalvard thresholding algorithms, demonstrate optimal performance in vegetation segmentation. The evaluation statistics of NRMSE and R² for the ExG&Otsu method as the most accurate method were obtained as 5.13 % and 0.96, respectively. Conversely, the RGBVI&RC method exhibits the least accuracy in the initial evaluation, with NRMSE and R² values of 8.18 % and 0.87, respectively. Comparative analysis of statistical indicators showed that the ExG&Otsu and ExG&RC methods with similar performance, displaying the highest correlation with ground truths. Additionally, the GLI&Otsu method consistently demonstrates the lowest error compared to ground truths. [ABSTRACT FROM AUTHOR]

Waterbody extraction from satellite imagery plays a crucial role in various environmental monitoring and management applications. Accurate identification and delineation of water bodies are essential for assessing water resources, monitoring changes in aquatic ecosystems, and supporting decision-making processes. This review presents a comprehensive analysis of different methods used for waterbody extraction from satellite images, highlighting their strengths, limitations, and recent advancements. This review begins by discussing traditional methods, such as thresholding-based methods, machine learning methods, and object-based image analysis, which have been widely employed in the past. Consequently, the focus shifts towards, how deep learning models, such as convolutional neural networks (CNNs) have been applied to improve waterbody extraction accuracy and address challenges posed by spectral variations, cloud cover, and sensor limitations. Overall, this review serves as a valuable resource for researchers, practitioners, and decision-makers involved in water resource management and environmental monitoring. [ABSTRACT FROM AUTHOR]

Fabric defect detection is closely related to quality control. Therefore, it is an important topic in the modern textile field. In this article, a new fabric defect detection method using ACS-based thresholding and GA-based optimal Gabor filter is proposed. The multi-level gray-scale image thresholding is improved by using Adaptive cuckoo search (ACS) to calculate the best threshold. In order to suppress texture interference, the optimal Gabor filter calculated by genetic algorithm (GA) is used to achieve the optimal feature extraction of fabric defects. A specific image acquisition system is set up to obtain clear defective fabric images and corresponding defect-free images which are used to build the database. The performance of the method is evaluated by extensive experiments on various types of fabric defects. Experimental results show that the proposed method, effective and robust, is superior than the other six methods. [ABSTRACT FROM AUTHOR]

The spread of lung tumors and their changes occur dynamically, so precise segmentation of the images obtained is necessary. In this study, an extended region growth algorithm was performed on CT lung tumor images to examine accurate tumor margin and area. At first, a new threshold was implemented in MATLAB software by defining a larger target region around the primary tumor. Then, nearby points were settled in an array and then these points were updated based on tumor growth to set the fresh tumor margins. By the algorithm, furthest distance from the center of color intensity point of the primary tumorous area was selected to grow the region. Afterwards, fresh tumor border was determined by interpolation between these refreshed points through drawing lines from the tumor region center. The edge correction was then applied and the obtained new region was attached to the main region to reach a segmented tumor exterior. This technique improved the tumor recognition by 96% accuracy. In the inclusive algorithm, the conformance percentage had a positive impact on the achievement of the threshold and the renewal of the relative amount by 13% over the accuracy score. Also compared to the basilar algorithm, at least 12% of the percent differences in conformity were found to segment the tumor region in lung CT images. The derived dice similarity coefficients were close to each other for both the basilar and inclusive algorithms by 0.79±0.05 and 0.88±0.04, correspondingly. The p-value of these dice coefficients was less than 0.08 resulting from the paired Student's t-test between two algorithms. The combination of methods such as machine learning is intended to improve segmentation accuracy for different types of nodule and tumor CT images. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Breast cancer is a prevalent disease and the second leading cause of death in women globally. Various imaging techniques, including mammography, ultrasonography, X-ray, and magnetic resonance, are employed for detection. Thermography shows significant promise for early breast disease detection, offering advantages such as being non-ionizing, non-invasive, cost-effective, and providing real-time results. Medical image segmentation is crucial in image analysis, and this study introduces a thermographic image segmentation algorithm using the improved Black Widow Optimization Algorithm (IBWOA). While the standard BWOA is effective for complex optimization problems, it has issues with stagnation and balancing exploration and exploitation. The proposed method enhances exploration with Levy flights and improves exploitation with quasi-opposition-based learning. Comparing IBWOA with other algorithms like Harris Hawks Optimization (HHO), Linear Success-History based Adaptive Differential Evolution (LSHADE), and the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and black widow optimization (BWO) using otsu and Kapur's entropy method. Results show IBWOA delivers superior performance in both qualitative and quantitative analyses including visual inspection and metrics such as fitness value, threshold values, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental results demonstrate the outperformance of the proposed IBWOA, validating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is commonly used in surgical training and development of medical robotics systems. Recent advancements in surgical training often utilize soft-tissue phantoms based on gelatin, with additional objects inserted to represent different, typically fluid-based pathologies. Segmenting these objects from the images is an important step in the development of training and robotic systems. The current study proposed a simple and fast algorithm for segmenting convex cyst-like structures from phantoms under very low training sample scenarios. The algorithm is based on a custom two-step thresholding procedure with additional post-processing with two trainable parameters. Two large phantoms with convex cysts are created and used to train the algorithm as well as evaluate its performance. The train/test procedures were repeated 60 times with different dataset splits and proved the viability of the solution with only 4 training images. The DICE coefficients were on average at 0.92, while in the best cases exceeded 0.95, all with fast performance in single-thread operation. The algorithm might be useful for development of surgical training systems and medical robotic systems in general. [ABSTRACT FROM AUTHOR]

Masked face segmentation tasks have become significantly more difficult due to the increasing use of face masks. On the other hand, the forehead, eyebrows, and eye regions are usually visible and reveal vital information. This exposed area of the face has been segmented and trusted to be used in real life for various applications, such as security, healthcare education, and projects in smart cities. The field of image segmentation has seen a significant increase in study in recent years, leading to the development of multi-level thresholding algorithms that have proven to be very successful compared to other approaches. Traditional statical techniques such as Otsu and Kapur are benchmark algorithms for image thresholding automation. The two techniques widely used, Otsu's and Kapur's entropy, are combined to create a hybrid fitness function to identify the ideal threshold values. In this study, we effectively reduce the computational time demonstrated by the high convergence curve while maintaining optimal outcomes by integrating the hybrid fitness function with multi-level thresholding using the Electric Eel Foraging Optimization (EEFO) approach to segment the uncovered region of masked face images. EEFO is a bio-inspired metaheuristic algorithm that simulates how electric EEL forages in nature. This algorithm achieved promising results in several optimization tasks, such as masked face segmentation. The proposed method is compared with ten cutting-edge algorithms focusing on recently developed metaheuristic techniques and outperforms them. Five metrics were used to evaluate the algorithm's performance: MSE, PSNR, SSIM, FSIM, and image quality index. The proposed method achieved superior results of 101.79, 26.83, 0.8058, 0.9339, and 0.9553 for average MSE, average PSNR, average SSIM, average FSIM, and average image quality index, respectively. Its superiority is verified by using the suggested approach on six benchmark images. The results demonstrate how effectively the proposed algorithm outperforms reliable metaheuristic approaches for solving masked face segmentation challenges.

Abstract Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

The work discusses a number of techniques for segmenting dermoscopic images of skin lesions to identify the areas occupied by these lesions. Segmentation is necessary as the first stage of most methods of computer diagnostics of malignancy of neoplasms. A number of techniques, such as ABCDE, use the shape of the tumor as one of the criteria for making a diagnosis; for others, such as the use of convolutional neural networks, identifying the tumor allows one to increase the accuracy of the results obtained. The work discusses three methods of segmentation: thresholding using Otsu's method to calculate the threshold value, a convolutional neural network built on the U-net architecture, and a similar convolutional neural network with an added attention mechanism. The advantages and disadvantages of each method are considered, as well as the possibility of using them together to obtain the best segmentation results.The paper considers the application of an algorithm based on a morphological projector for determining structural differences for comparing dermoscopic images. This will allow to identify changes that have occurred in skin lesions over time, for a more accurate diagnosis of their malignancy. The proposed algorithm makes it possible to detect differences in images even if there is a significant difference in the brightness and color levels of the compared images, and also ignores small insignificant details, such as noise, dermatoscope optics marks, hair, etc. A method for correcting the desynchronization of images using the structural similarity index as a similarity metric, and the sine-cosine algorithm as an optimization algorithm is proposed. The proposed algorithms were tested on dermatoscopic images and the possibility of their application was demonstrated.

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries. [ABSTRACT FROM AUTHOR]

The thresholding problem is studied in the context of graph theoretical analysis of gene co-expression data. A number of thresholding methodologies are described, implemented, and tested over a large collection of graphs derived from real high-throughput biological data. Comparative results are presented and discussed. [ABSTRACT FROM AUTHOR]

This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental results suggest that the proposed method generates comparable results to some of the best methods in literature. However, unlike many other methods, it works well on different types of MRI scans. Moreover, this method generates the skull mask along with the brain mask that can be used to study various skull pathologies. [ABSTRACT FROM AUTHOR]

Summary: Microbiome data typically lie in a high‐dimensional simplex. One of the key questions in metagenomic analysis is to exploit the covariance structure for this kind of data. In this paper, a framework called approximate‐estimate‐threshold (AET) is developed for the robust basis covariance estimation for high‐dimensional microbiome data. To be specific, we first construct a proxy matrix Γ$$ \boldsymbol{\Gamma} $$, which is almost indistinguishable from the real basis covariance matrix ∑$$ \boldsymbol{\Sigma} $$. Then, any estimator Γ^$$ \hat{\boldsymbol{\Gamma}} $$ satisfying some conditions can be used to estimate Γ$$ \boldsymbol{\Gamma} $$. Finally, we impose a thresholding step on Γ^$$ \hat{\boldsymbol{\Gamma}} $$ to obtain the final estimator ∑^$$ \hat{\boldsymbol{\Sigma}} $$. In particular, this paper applies a Huber‐type estimator Γ^$$ \hat{\boldsymbol{\Gamma}} $$, and achieves robustness by only requiring the boundedness of 2+ϵ$$ \epsilon $$ moments for some ϵ∈(0,2]$$ \epsilon \in \left(0,2\right] $$. We derive the convergence rate of ∑^$$ \hat{\boldsymbol{\Sigma}} $$ under the spectral norm, and provide theoretical guarantees on support recovery. Extensive simulations and a real example are used to illustrate the empirical performance of our method. [ABSTRACT FROM AUTHOR]

Graphic Processing Units (GPUs) are becoming very important in the present day. Their high computational capabilities with high speed and accuracy are making them a very strong force in communication engineering. In recent times, their need has increased tremendously due to the increasing range of applications. Video surveillance is an important field where very heavy computations are needed to be done on videos to perfectly detect the motion of an object in suspicious situations. The various analyses on video can be used to extract information and process data to generate actionable intelligent conclusions. However, CPUs fail to deliver real time results when it comes to high-resolution videos from a large number of cameras simultaneously. Thankfully, there is a lot of graphic hardware available nowadays, which comprises powerful hardware processors often intended to process data in parallel and so greatly accelerates the processes being done on them. An accelerated algorithm is required for processing petabytes of data from security cameras and video surveillance satellites and that in real time. In this paper, we propose a method of using GPUs in detecting the motion of an object at different junctions in video surveillance. The results show a great gain in performance when the proposed method runs on GPUs and CPUs in terms of speed as well as accuracy. The new parallel processing approaches are developed on each phase of the algorithm to enhance the efficiency of the system. Proposed algorithm achieved an average speed up of 50.094x for lower resolution video frames (320 × 240,720 × 480,1024 × 768) and 38.012x for higher resolution video frames (1360 × 768,1920 × 1080) on GPU, which is superior to CPU processing. [ABSTRACT FROM AUTHOR]

This work considers an approach to solving the problem of noise removal in a large dataset from sparsity class under conditions of weak dependence based on controlling the false discovery rate. An upper asymptotic bound for the RMS risk is obtained. [ABSTRACT FROM AUTHOR]