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Image processing is a promising technique for enhancing images or extracting useful information from them. One commonly used density‐based clustering algorithm is DBSCAN (Density‐Based Spatial Clustering of Applications with Noise). However, DBSCAN struggles with satellite images due to their large sizes, often resulting in excessively long computation times. This research proposes an improved version of DBSCAN called "Enhanced DBSCAN‐based Histogram" (EDBSCAN‐H) to address these issues. EDBSCAN‐H enhances DBSCAN by incorporating a histogram‐based approach to better manage large datasets and reduce computation time. The key improvement lies in using the histogram of the input image and measuring the distance between data objects and histogram points to determine whether a region is dense or sparse, thereby selecting suitable parameters. EDBSCAN‐H introduces an additional parameter, ε2, alongside the original DBSCAN parameters ε₁ and MinPts. This enhancement allows EDBSCAN‐H to achieve improved performance across various metrics for clustering spatial data images. [ABSTRACT FROM AUTHOR]

The current methods for detecting joints on tunnel face rely primarily on manual sketches, which are associated with issues of low detection efficiency and subjectivity. To address these concerns, this paper presents an intelligent recognition and segmentation algorithm based on Mask R-CNN (mask region-based convolutional neural network) for detecting joint targets on tunnel face images and automatically segmenting them, thereby improving detection efficiency and objectivity of the results. Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. The algorithm was trained on a dataset of 800 tunnel face images, and the research findings demonstrate that it can quickly detect the position of joints on tunnel face images and assign masks to the joint pixel regions to achieve joint segmentation. The mean average precision (mAP) of the detection boxes and segmentation in the 80 test set images were 58.0% and 49.2%, respectively, which outperforms the original Mask R-CNN algorithm and other intelligent recognition and segmentation algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c‐means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C‐Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley's segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

Let G = (V, E) be a connected, basic, and finite graph. A subset T = u 1 , u 2 , ... , u k of V(G) is said to be a resolving set if for any y ∈ V(G), the code of y with regards to T, represented by C T y , which is defined as C T y = d u 1 , y , d u 2 , y , ... , d u k , y , is different for various y. The dimension of G is the smallest cardinality of a resolving set and is denoted by dim(G). If, for any t ∈ V – S, there exists r ∈ S such that S – r ∪ t is a resolving set, then the resolving set S is secure. The secure metric dimension of is the cardinal number of the minimum secure resolving set. Determining the secure metric dimension of any given graph is an NP-complete problem. In addition, there are several uses for the metric dimension in a variety of fields, including image processing, pattern recognition, network discovery and verification, geographic routing protocols, and combinatorial optimization. In this paper, we determine the secure metric dimension of special graphs such as a globe graph G l n , flag graph F l n , H- graph of path P n , a bistar graph B n , n 2 , and tadpole graph T 3 , m . Finally, we derive the explicit formulas for the secure metric dimension of tadpole graph T n , m , subdivision of tadpole graph S T 3 , m , and subdivision of tadpole graph S T n , m . [ABSTRACT FROM AUTHOR]

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501. [ABSTRACT FROM AUTHOR]

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods' performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods. [ABSTRACT FROM AUTHOR]

Creating a dependable and effective digital image watermarking (WM) method necessitates balancing imperceptibility, resilience, capacity, and security. Several studies have combined spatial and transform domains to meet these needs. We investigated the current state of hybrid digital image WM. When developing a hybrid WM strategy, it is critical to consider software compatibility. Following a brief review of the literature, we used a table to analyse and evaluate current hybrid approaches. Furthermore, the limitations and possibilities of these approaches are discussed. We investigated the limitations of current research methodologies and proposed new research directions. The internet's role in distributing digital material has resulted in more digitalisation and more complex copyright concerns. Copyright breaches are being reduced through the deployment of innovative digital WM techniques. As a result of this research, a WM system capable of dealing with a wide range of threats is being developed. The goal of this study is to maximise security while maintaining visibility and resilience. Discrete wavelets transform (DWT) and singular value decomposition (SVD) were used to investigate covert operations; DWT was used to isolate each level of the host image, which was then processed using SVD. At the time, normalized correlation was the best method for measuring watermarked images. In research, DWT–SVD was more accurate in detecting assaults; it shows structural similarity index and normalized correlation are 98.4 and 98.35, respectively. Watermarked images can withstand a variety of assaults due to their invisibility and resilience; this system repels assaults that alter pixel values better than traditional methods. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160×160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes., Author(s): Heshan Zhang [1]; Xin Tan [1]; Mengwei Fan [1]; Cunshu Pan [1]; Zhanji Zheng [1]; Shuang Luo [1]; Jin Xu (corresponding author) [1] 1. Introduction With the rapid development [...]

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils. [ABSTRACT FROM AUTHOR]

Objective. Histopathological examinations will diminish as minimally invasive epilepsy surgery increasingly replaces open surgery. The objective of this study was to test if visual and computer-aided quantitative analyses of presurgical high-quality 3 Tesla MRIs complying with the International League Against Epilepsy (ILAE) Neuroimaging Task Force recommendations can inform on histopathological diagnosis. Methods. Ninety-two patients from Copenhagen and Oslo University Hospitals fulfilled patient-, imaging-, and histopathological inclusion criteria: 69 patients were diagnosed with hippocampal sclerosis (HS) ILAE type 1 or 2, and 23 patients had normal appearing hippocampi or other histopathology than HS (no-HS). MRIs from 52 healthy controls (HC) were included. Image processing was performed in FreeSurfer v.6.0 with the built-in cross-sectional hippocampal subfield segmentation tool and multimodal MRI input. Volume outputs were used to calculate volume asymmetry ratios (VARs) for whole hippocampus (WH) and subfields. Results. HS patients had significantly larger WH VARs compared to no-HS patients and HC, with a sensitivity = 0.93 and specificity = 1.0 for histopathological HS diagnosis. Visual MRI assessment yielded a sensitivity = 0.90 and specificity = 0.96 for histopathological HS diagnosis. CA1 and CA4 VARs and the number of seizure-free patients were not significantly different in HS ILAE type 1 compared to type 2 patients. Significance. FreeSurfer analyses of presurgical MRIs are excellent at separating patients histopathologically diagnosed with HS from patients with other pathology or normal appearing hippocampi. Using the FreeSurfer hippocampal subfield segmentation tool did not allow for separating HS ILAE subtypes. [ABSTRACT FROM AUTHOR]

In this study, different strategies used to count vehicles and people in different image areas at a street intersection were analyzed to obtain counts at appropriate times suitable for real-time control of a traffic light. To achieve this, video recordings of cameras placed at the intersection were used to test and verify image processing algorithms and deep learning using the YOLOv3 network implemented on a 4 GB RAM Jetson Nano card. We counted the vehicles and people that stopped and crossed the polygons to delimit the different areas of interest, with a maximum error of ±2 in the validation tests for all cases. In addition, as a strategy, we combined the images from both cameras into a single one, thereby allowing us to make a single detection and subsequently determine if they are inside or outside the polygons used in separating the areas of interest with the respective counts. Furthermore, this enabled us to obtain information on vehicles and people stopped and crossing in a time of 0.73 s on average. Hence, it was established that the inclusion of the control algorithm is appropriate for real-time control of traffic lights. [ABSTRACT FROM AUTHOR]

Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum-visual-sensing acquisition system and an image-processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut-off composite dimming and near-infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high-speed charge-coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image-processing fusion model based on the spatial information fuzzy C-means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image-processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual-sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM-Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high cleaning intensity, low efficiency, and hidden safety hazards of high-altitude curtain walls, this study proposes that the image processing method is a kind of image processing technology in human-computer collaborative visual design. The algorithm uses generalized mapping to scramble the picture and then expands and replaces the scrambled pictures one by one through the image processing technical support system. Studies have shown that this calculation method has mixed pixel values, good diffusion performance, and strong resistance performance. The pixel distribution of the processed image is relatively random, and the features of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance. [ABSTRACT FROM AUTHOR]

Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in these systems, the computational power of the processors is not considered. As a result, in these heterogeneous systems for image processing applications, an uneven workload distribution issue is found. A workload distribution programming framework is presented and discussed in this paper. The proposed strategy consists of two parts. As a first step, image data is split into optimal split sizes and distributed across nodes, then the image data is distributed across CPU and GPU in a second step for processing. A heterogeneous environment is created by combining the CPU and GPU. The OpenCL Java bindings are used to set up both the CPU and GPU to run the program. To assess the performance of the suggested technique, certain tests are carried out and compared to current platforms. For image processing applications in heterogeneous clusters, the proposed workload distribution approach distributed image data efficiently. The results of the proposed solution (Hadoop + GPU) show that using an effective workload allocation mechanism in heterogeneous systems reduces average execution time while improving overall application performance. [ABSTRACT FROM AUTHOR]

Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection. [ABSTRACT FROM AUTHOR]

Civil steel structures and infrastructures, such as truss railway bridges, are often subject to potential damage, mainly due to fatigue phenomena and corrosion. Therefore, damage detection algorithms should be designed and appropriately implemented to increase their structural health. Today, the vast amount of information provided by data processing techniques and measurements coming from a monitoring system constitutes a possible tool for damage identification in terms of both detection and description. For this reason, the research activity aims to develop a methodology for a preliminary description of the damage in steel railway bridges induced by fatigue phenomena. The proposed approach is developed through an integration of global and local procedures. At the global scale, vibration-based procedures will be applied to improve a forecast numerical model and, subsequently, to identify the zones most involved in fatigue problems. At the local scale, careful and refined local identification will be pursued via image processing techniques whose evidence will be analyzed and described through nonlinear numerical models. A case study of a historical railway bridge in Spain will illustrate the methodology's performance, potentiality, and critical issue. [ABSTRACT FROM AUTHOR]

Background. Though treatable, osteoporosis continues as a substantially underdiagnosed and undertreated condition. Bone mineral density (BMD) monitoring will definitely aid in the prediction and prevention of medical emergencies arising from osteoporosis. Although quantitative computed tomography (QCT) is one of the most widely accepted tools for measuring BMD, it lacks the contribution of bone architecture in predicting BMD, which is significant as aging progresses. This paper presents an innovative approach for the prediction of BMD incorporating bone architecture that involves no extra cost, time, and exposure to severe radiation. Methods. In this approach, the BMD is predicted using clinical CT scan images taken for other indications based on image processing and artificial neural network (ANN). The network used in this study is a standard backpropagation neural network having five input neurons with one hidden layer having 40 neurons with a tan-sigmoidal activation function. The Digital Imaging and Communications in Medicine (DICOM) image properties extracted from QCT of human skull and femur bone of rabbit that are closely associated with the BMD are used as input parameters of the ANN. The density value of the bone which is computed from the Hounsfield units of QCT scan image through phantom calibration is used as the target value for training the network. Results. The ANN model predicts the density values using the image properties from the clinical CT of the same rabbit femur bone and is compared with the density value computed from QCT scan. The correlation coefficient between predicted BMD and QCT density valued to 0.883. The proposed network can assist clinicians in identifying early stage of osteoporosis and devise suitable strategies to improve BMD with no additional cost. [ABSTRACT FROM AUTHOR]

Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data. [ABSTRACT FROM AUTHOR]

One of the most fundamental subjects in nanoscience and nanotechnology is structural analysis. We employed a scanning electron microscope (SEM) image of the manufactured Gd3+/13X/DOX/FA nanocomposite in this study. The size, dimensions, and morphology of nanocomposite materials were studied to ensure the uniformity and homogeneity of SEM images. This is the first study to look at segmented SEM images for fractal dimension (FD) and other statistical criteria, including average, maximum, minimum, skewness, and range for magnetic resonance imaging (MRI) nanocomposite. The average of FD (FDavg), the standard deviation of FD (FDsd), and the lacunarity of FD (FDlac) fractal data analysis criteria were also employed. The findings show that particle sizes and shapes vary because the minimum-to-maximum range is not zero, and our data provide a reasonable range. This interpretation is further supported by an analysis of the nanocomposite's SEM image. At first glance, the image seemed to be uniform. However, when the calculations were performed, it was discovered that the generated particles were not particularly uniform. The particles were uniformly dispersed throughout all surfaces, although their sizes, dimensions, and morphologies varied. In conclusion, the study was supported by fractal data analysis, emphasizing the importance of structural analysis for future research, particularly for medical applications like MRI. [ABSTRACT FROM AUTHOR]

Along with the increase of electricity consumption in society, it means that the quality of power inspection gives higher requirements. The traditional manual inspection method is difficult to ensure the stability of checking quality. Without the popularization of manned distribution stations and smart grid methods, replacing manual inspection with electric power inspection robots can improve the inspection quality and inspection efficiency to a certain extent. Along with theimage processing process which is widely used in the robot system, visual impact is not only an effective way for us to obtain information but also the key reflection of the robot system intelligence. Unmanned aircraft as an extremely important robot has long been widely used in all walks of life. Power inspection is the most common use of unmanned aircraft robot inspection. However, due to extreme criteria such as bad weather, relative motion, and shaking of imaging equipment, inspection images are obtained ambiguously. The quality of the inspection image is related to the timely understanding, analysis, and judgment of the power engineering quality inspection database. In the paper, the unmanned aircraft power inspection robot as a scientific research foothold, according to explore the key technology of anti-internet technology to clear the image ambiguity, proposed a complete disambiguation optimization calculation method. The image denoising optimization calculation method based on the directional characteristics of time-frequency analysis and edge maintenance is selected to carry out the finding of the image, which reasonably solves the image resolution bottleneck problem of GIS in unmanned aircraft robot line inspection. Meanwhile, with the emergence of nanorobots in the 21st century, the application and analysis of remote sensing image processing technology in the field of robotic power inspection in this paper will also bring new vitality and vigor to the research of nanopower inspection robots and solve the problem of unclear images existing in nanorobots. [ABSTRACT FROM AUTHOR]

This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm. [ABSTRACT FROM AUTHOR]

Nasal bone is a long, paired series of small bones, which is narrow at the top and broad at the bottom, that forms the base of the nasal dorsum. Together with the nasal part of the frontal bone, the frontal process of the maxilla and the middle plate of the ethmoid bone constitute the bone scaffold of the external nose. In this paper, the DICOM image data file was imported into the Mimics software for 3D reconstruction. At the same time, the Geomagic software was used for relevant image processing, and the finite element software ANSYS was used to establish a finite element model to analyze the stress characteristics of the nasomaxillary complex. Results. The maximum principal stress and maximum strain force at the lower segment of nasal bone and the junction of nasal bone and maxilla were relatively large. When the same external force acts on the lower segment of the nasal bone and the angle is 0° (sagittal force), the maximum principal stress and maximum strain force are the smallest. When the angle continues to increase, the maximum principal stress and maximum strain force continue to increase. [ABSTRACT FROM AUTHOR]

With the concept of quality education put forward, students' sports activities have received extensive attention from society. As a result, sports injuries among students during sports have also aroused widespread concern, so it is an irreversible trend to detect sports injuries. The development of multimedia intelligent 3D image technology also provides technical support for sports injury detection, which makes it possible to automatically detect sports injuries. In this paper, an automatic detection system for sports injuries was designed based on multimedia intelligent three-dimensional image technology, and the related content was evaluated. In the investigation of the parts of students' sports injuries, it was concluded that the injury rate of the students' ankle joints was the highest; in the investigation of the types of sports injuries among students, it was concluded that students were more likely to suffer from joint sprains; in the project investigation of students' sports injuries, it was concluded that students were more prone to sports injuries in ball games with a large amount of exercise; in the investigation of the causes of students' sports injuries, it was concluded that the main reasons for students' sports injuries were physical insufficiency and a bad venue environment; in terms of the performance evaluation of the sports injury detection system, it was concluded that the accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology had been improved to different degrees compared with the traditional sports injury detection methods. Therefore, the detection efficiency of the sports injury automatic detection system proposed in this paper was improved by 5.7% compared with the traditional sports injury detection method. [ABSTRACT FROM AUTHOR]

With the development of film and television industry and the rise of new media short video, film and television postproduction process has higher and higher requirements for image quality. In film and television postproduction, optimizing the image quality can enhance the resolution and make the image more vivid and detailed. High-quality image can fully embody the value of film and television, as well as promote the development of new media short videos. This paper optimizes image quality by improving image processing technology, thus improving the quality and value of film and television and new media short videos. In this paper, a convolutional neural network combined with a nonlinear activation function is used to establish an improved image processing technology model to efficiently extract image features. This technology can enhance the ability to extract image features, improve the accuracy of image feature extraction, and then improve the image resolution and details, thus improving the image quality in the process of film and television postproduction. The results show that the average value of PSNR is 30.29. The average value of PSNR of the proposed algorithm is higher than that of other algorithms, indicating that the error between the image processed by the proposed algorithm and the original image is small. The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. Compared with other algorithms, the structure processed by the algorithm in this paper is more similar to the original structure, resulting in a better graph. The algorithm in this paper has the best performance on both the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM). The improved image processing technology proposed in this paper can effectively improve the accuracy of image feature extraction, making film and television or new media short video images of higher quality. [ABSTRACT FROM AUTHOR]

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning in recent years, it has shown excellent performance in various image and video processing tasks. In addition, it also has a great role in promoting the spatio-temporal fusion of remote sensing images. The reconstructed image can give people a good visual experience. The invention relates to a remote sensing image fusion method based on a progressive cascade deep residual network and provides an end-to-end progressive cascade deep residual network model for remote sensing image fusion. The use of the MSE loss function may cause oversmoothing of the fused image, so a new joint loss function is defined to capture finer spatial information to improve the spatial resolution of the fused image. Resize-convolution is used to replace the transposed convolution to eliminate the checkerboard effect in the fused image caused by the transposed convolution. Through the experiments on the remote sensing image fusion simulation and real datasets of multiple satellites, the data results of the proposed algorithm are more than 5.25% better than those of the comparative algorithm in the average quantification. The calculation time and system resource occupation are also reduced, which has important theoretical significance and application value in the field of artificial intelligence and image processing. It will play a certain role in promoting the theoretical research and application of remote sensing image fusion. [ABSTRACT FROM AUTHOR]

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world's major scientific and technological powers to develop. Although China's satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world's advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN's recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments. [ABSTRACT FROM AUTHOR]

Accurately and rapidly measuring the diameter of central serous chorioretinopathy (CSCR) lesion area is the key to judge the severity of CSCR and evaluate the efficacy of the corresponding treatments. Currently, the manual measurement scheme based on a single or a small number of optical coherence tomography (OCT) B-scan images encounters the dilemma of incredibility. Although manually measuring the diameters of all OCT B-scan images of a single patient can alleviate the previous issue, the situation of inefficiency will thus arise. Additionally, manual operation is subject to subjective factors of ophthalmologists, resulting in unrepeatable measurement results. Therefore, an automatic image processing method (i.e., a joint framework) based on artificial intelligence (AI) is innovatively proposed for locating the key boundary points of CSCR lesion area to assist the diameter measurement. Firstly, the initial location module (ILM) benefiting from multitask learning is properly adjusted and tentatively achieves the preliminary location of key boundary points. Secondly, the location task is formulated as a Markov decision process, aiming at further improving the location accuracy by utilizing the single agent reinforcement learning module (SARLM). Finally, the joint framework based on the ILM and SARLM is skillfully established, in which ILM provides an initial starting point for SARLM to narrow the active region of agent, and SARLM makes up for the defect of low generalization of ILM by virtue of the independent exploration ability of agent. Experiments reveal the AI-based method which joins the multitask learning, and single agent reinforcement learning paradigms enable agents to work in local region, alleviating the time-consuming problem of SARLM, performing location task in a global scope, and improving the location accuracy of ILM, thus reflecting its effectiveness and clinical application value in the task of rapidly and accurately measuring the diameter of CSCR lesions. [ABSTRACT FROM AUTHOR]

Deep learning (DL) approaches have been increasingly adopted to design antenna autonomously. For obtaining geometry of the broadband quasi-Yagi antenna from its physic response images directly, we propose an inverse design approach based on the optimized bidirectional symmetry GoogLeNet, which can extract the required bandwidth information to redesign the geometric parameters of antenna without changing its physical structure. It demonstrates that the bandwidth of a reference quasi-Yagi antenna is improved from 0.6 GHz to 1.15 GHz through the proposed inverse design DL approach, and the measured bandwidth value of this redesigned quasi-Yagi antenna achieves 1.16 GHz, which is improved 93% actually. The numerical and measured results indicate that the proposed DL approach could significantly improve the performance of the existed quasi-Yagi antenna and present a new attempt to apply the image processing techniques in resolving physical problem. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye's microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection. [ABSTRACT FROM AUTHOR]

Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph's nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effectively. Meanwhile, privacy protection plays a more meaningful role in analyzing graph-structured data such as social networks. Hence, this paper proposes PPFL-GNN, a novel privacy-preserving federated graph neural network framework for node representation learning, which is a pioneer work for graph neural network-based federated learning. In PPFL-GNN, clients utilize a local graph dataset to generate graph embeddings and integrate information from other collaborative clients to utilize federated learning to produce more accurate representation results. More importantly, by integrating embedding alignment techniques in PPFL-GNN, we overcome the obstacles of federated learning on non-IID graph data and can further reduce privacy exposure by sharing preferred information. [ABSTRACT FROM AUTHOR]

This study describes important techniques for production of a series of video signals for use in fine focusing operations and near-perfect astigmatism correction in the general-purpose scanning electron microscopy (SEM) field. These techniques can enhance the stability of the signal greatly when used for focusing. As two particularly important fundamental techniques, SEM image acquisition with priority given to the signal-to-noise ratio and signal reinforcement based on the active image processing concept were utilized fully. The performance improvement was evaluated using the case of a previously reported support system for fine focusing and astigmatism correction based on image covariance. The method is almost completely robust against noise within practical limits and allows for focusing and astigmatism correction for even extremely noisy SEM images. The results of this study may be useful not only in the SEM field but also in many fields that use weak signals. [ABSTRACT FROM AUTHOR]

Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features. [ABSTRACT FROM AUTHOR]

In the era of high-speed information, with the popularization of network and the rapid development of multimedia communication technology, human society has entered a key new stage of development. Human productivity and living style have been significantly altered as a result of social informatization. The field of computer image processing has also made significant strides recently. Photoshop has evolved into a must-have piece of software for those who work in computer graphics and image processing on a regular basis. It contributes to increased job productivity in the processing of computer visuals and photos because of its extensive functionality, user-friendliness, and humanization. Photoshop is a graphics and image processing technology programme that is very efficient. This software's image processing function is quite strong, and it is capable of producing graphics and image processing of the highest quality. This technology is widely used in practical work. Professionals are required to operate skillfully and grasp the technology accurately. Photoshop is widely used in current graphics and image processing. While meeting the relevant processing requirements of users, it is also used in various occasions related to image processing. [ABSTRACT FROM AUTHOR]

With the rapid development of image processing technology, image recognition systems based on massive image data are being developed and deployed. The wrong decision regarding an image recognition system for security-sensitive systems can cause serious problems such as personal accidents and property damage. Furthermore, adversarial attacks, which are security attacks that cause malfunctions in image recognition systems by inserting adversarial noise, have emerged and evolved. Several studies have been conducted to prevent adversarial attacks. However, existing mechanisms have low classification accuracy and low detection accuracy for adversarial examples with small adversarial noise. This paper proposes an adversarial example detection mechanism based on image feature extraction and a deep neural network (DNN) model. The proposed system achieves versatility and independence by detecting adversarial examples based on image features, such as edge noise and discrete cosine transform (DCT) bias, which adversarial examples have in common. The proposed system shows relatively higher detection accuracy than existing mechanisms for various types and amounts of adversarial noise and different sharpness of adversarial examples because the proposed system detects them depending on the characteristics of each type of adversarial example. [ABSTRACT FROM AUTHOR]

The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873–0.973) and 0.873 (95% CI: 0.757–0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531–0.902), PET-demographic data model (0.802, 95% CI: 0.633–0.970), and CT-PET model (0.797, 95% CI: 0.666–0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients. [ABSTRACT FROM AUTHOR]

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject's hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation. [ABSTRACT FROM AUTHOR]

This research study aims to measure the study analysis on the path of artisan spirit related to party education related to the ideological in colleges and universities. The image processing technology research study describes that primary data related to the artisan spirit performance were gathered using different questions related to the image processing technology. The party ideological education is the dependent variable, and image processing technology includes segmentation quality factors and performance related to the image processing technology. These are considered independent variables. For measuring, the data used AMOS software and run different results related to the party ideological education in college and universities. The character design major has more significant commercial benefits and chances for growth. The investigation and analysis can confirm that the main is appropriately orientated. It maintains continual communication with companies, scheduling, increasing students' practical competence, and also offers students internship opportunities. The overall results found that the artisan path to party ideological education significantly relates to college and universities based on the image processing technology. [ABSTRACT FROM AUTHOR]

Image processing is an important domain for identifying various crop varieties. Due to the large amount of rice and its varieties, manually detecting its qualities is a very tedious and time-consuming task. In this work, we propose a two-stage deep learning framework for detecting and classifying multiclass rice grain varieties. A series of steps is included in the proposed framework. The first step is to perform preprocessing on the selected dataset. The second step involves selecting and fine-tuning pretrained deep models from Darknet19 and SqueezeNet. Transfer learning is used to train the fine-tuned models on the selected dataset. The 50% sample images are employed for the training and rest 50% are used for the testing. Features are extracted and fused using a maximum correlation-based approach. This approach improved the classification performance; however, redundant information has also been included. An improved butterfly optimization algorithm (BOA) is proposed, in the next step, for the selection of the best features that are finally classified using several machine learning classifiers. The experimental process was conducted on selected rice datasets that include five types of rice varieties and achieves a maximum accuracy of 100% that was improved than the recent method. The average accuracy of the proposed method is obtained at 99.2%, through confidence interval-based analysis that shows the significance of this work. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well. [ABSTRACT FROM AUTHOR]

Computer vision and image processing techniques are considered efficient tools for classifying various types of fruits and vegetables. In this paper, automated fruit classification and detection systems have been developed using deep learning algorithms. In this work, we used two datasets of colored fruit images. The first FIDS-30 dataset of 971 images with 30 distinct classes of fruits is publicly available. A major contribution of this work is to present a private dataset containing 761 images with eight categories of fruits, which have been collected and annotated by ourselves. In our work, the YOLOv3 deep learning object detection algorithm have been used for individual fruit detection across multiple classes, and ResNet50 and VGG16 techniques have been utilized for the final classification for the recognition of a single category of fruit in images. Next, we implemented the automatic fruit classification models with flask for the web framework. We got 86% and 85% accuracies from the public dataset with ResNet50 and VGG16, respectively. We achieved 99% accuracy with ResNet50 and 98% accuracy with the VGG16 model on the custom dataset. The domain adaptation approach is used in this work so that the proposed deep learning-based prediction model can cope with real-world problems of diverse domains. Finally, an Android smartphone application has been developed to classify and detect fruits with the camera in real-time. All the images uploaded from the Android device are automatically sent and consequently analyzed on the web, and finally, the processed data and results are returned to the smartphone. The custom dataset and implementation codes will be available after the manuscript has been accepted. The custom dataset can be found at: https://github.com/SumonAhmed334/dataset_fruit. [ABSTRACT FROM AUTHOR]

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures. [ABSTRACT FROM AUTHOR]

As an important part of cultural heritage, murals reflect the economy, culture, and ideas of different historical periods and are an important basis for historical research. The lines in murals are the core elements to express the beauty of images. They have an irreplaceable special position in murals and are of great significance in the protection and restoration of murals. With the development of image recognition technology, the recognition of mural images has become a key research topic. In recent years, as a new image processing technology, deep learning based on a convolutional neural network is widely used in many fields. Using a convolutional neural network to recognize images has become a very active topic. With the continuous deepening of the number of layers of the convolutional neural network model, its autonomous learning ability of image recognition continues to improve. However, there are still some problems in the current image recognition model based on a convolutional neural network for mural images with rich structural details and complex texture and color. Therefore, according to the texture and structural characteristics of mural images, this paper uses the design idea of a convolutional neural network for reference to carry out research on mural image recognition. The improved algorithm proposed in this paper is tested on the experimental data set of mural images. The experimental results show that the improved algorithm can reduce the recognition error; enhance the edge, texture, and structure information of the reconstructed mural image; and enrich the detail information of the reconstructed mural image. [ABSTRACT FROM AUTHOR]

Dance sport is a competition of athletes for qualities such as speed, strength, endurance, flexibility, and coordination, requiring a high level of overall physical fitness. As China's sports fever continues to heat up, especially among students, the active nature of young people can lead to overenthusiasm for sports, dance, and other sports, which can lead to various physical discomforts during sports. Especially in the current sports habits of students, there is a great hidden danger of sports injuries. It is mainly due to excessive exercise, irregular exercise methods, and incomplete sports protective equipment. On this basis, this study proposes a model to further assess the health status of individuals and groups from the rate of energy consumption during exercise by building a real-time motion detection system. The development of sports health assessment systems, including the application of imaging techniques, is also analyzed. The experimental results in this article show that when the error threshold is set to 10%, the accuracy of the model is 85%, and the obtained results are ideal. [ABSTRACT FROM AUTHOR]

Combustion and flame characteristics of laminar methane/air and n-butane/air flames in a 3D-printed micro-slot burner is compared and reported in this study. The stability limit, flame appearance, and emission performance are investigated experimentally. In addition, past research on conventional burners is compared with the results of this study throughout the paper. The construction of this micro-slot burner was met by selective laser melting (SLM) process. Flame characteristics such as lift-off height, length, visible area, maximum width, and neck width are obtained using an image processing algorithm and are examined at different fuel and airflow rates. The results show that the blow-out limits of methane/air and n-butane/air flames are almost the same when compared at the same volume flow rates, although the methane/air flames are more stable than n-butane/air flames at the same thermal input powers. A region of interesting rope-like oscillatory flames (that has never been seen before in conventional burners) is observed in a small portion of a stable region for n-butane with a period ranging from 75.0 to 210.0 m s. It is also observed that the fuel type and fuel and airflow rates affect the flame shape and appearance and the flames formed by heavier fuel (n-butane) have longer length, lift-off height, maximum width, and visible area and lower neck width. Furthermore, methane/air flames exhibit lower values of C O and higher values of N O x in the flue gas when compared to n-butane/air flames. [ABSTRACT FROM AUTHOR]

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The global community is now coping with such a significant issue as the Covid-19 virus, public gatherings are experiencing certain restrictions in order to stop the virus from spreading further. The issue takes on a bigger significance during religious pilgrimages such as the Hajj and the Umrah, when tens of thousands, if not hundreds of thousands, of people gather in holy cities to participate in religious rituals. During such a time period, it is quite difficult to single out an infected person from among the big crowd that is there. The current screening approach only includes a single element of identity, which means that there is a possibility that the screening process may fail because there will not be enough identification. The use of thermal imaging provides a higher level of accuracy when compared to more conventional ways of testing for viral infections in the detection of these symptoms in crowded locations. The primary method that is utilised to determine whether or not a person is infected with the virus is an image processing algorithm that is built in MATLAB. The first step in the process of acquiring an image is to divide the video that is being captured into individual frames. Following this step, the frames that have been focussed are processed in a number of ways. The temperature of a person's body may be estimated by taking a thermal image and then using the RGB separation feature on it. In order to categorise and sort the data, the k-means approach was used as part of the segmentation operation. In addition to eliminating the skin frequency, it also gets rid of the background noise, which often has a higher frequency than the skin frequency. The Viola–Jones technique, which may be used to identify the person's breathing rate, can be used to locate the end of a person's nose, specifically the tip of the nose. The Cascaded Adaboost Classifier is an option that may be used to finish the classification process after the operation has been completed. The suggested method has an accuracy rate of 89.23 percent and a simulation period of around 60 seconds, which guarantees the safety of huge groups of people's public health. [ABSTRACT FROM AUTHOR]

This article, titled "Retracted: Sports Action Recognition Based on Image Processing Technology and Analysis of the Development of Sports Industry Pattern," has been retracted by Wiley due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, availability of data, inappropriate citations, and incoherent or irrelevant content. As a result, the reliability of the article's content is compromised. The corresponding author has been given the opportunity to respond to this retraction. [Extracted from the article]