Search

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]

Within a few decades of development, Internet is been refreshing the world's awareness of its development and potentials at an unexpected speed. In recent years, virtual reality technology has begun to be applied to more and more fields. Especially in instant transmission of information, the applications of VR are incomparable. Akin to the wide-ranged advantages of the technology in education and health, the applications of VR in the art of artistic image designing need to be addressed. With this research work, a comprehensive study is presented about the applications of virtual reality in the art of image visualization. The research intends to analyze the possibility of intermingling modern image visualization with the emerging VR technology. Following a systematic approach, the history and development of display design are reviewed besides studying the artistry techniques used in the technology of art image visualization design. The interdisciplinary association of the two fields is explored with the help of relevant algorithms. The model proposed in the paper for the design of art image is based on the visual interactive process, rigorous assembly sequence, and appropriate equipment mode. As a result, the whole design process is more controllable and feasible. The suggested design concept is verified dynamically. Through the collection and comparison of feedback information, the hypothesis algorithm of the model is updated to accommodate the dynamic information so that to reflect the complete image design process. To assess performance of the model, evaluation was conducted from various perspectives. The three basic image operations such as illumination change, occlusion change, and color change were performed by using the two technologies such as the traditional image design and VR-based image design. From the comparison of experimental data, it was revealed that the effects of traditional art image visualization products produced by the art image visualization and that of VR technology are quite different. For instance, the visualization score achieved for the lighting change operation using the VR technology was 95.48%, whereas 85.57% was obtained for the same operation using the traditional image design technology. Similarly, while adding visual effects using the VR technology, an average of 9% higher score was obtained for the said image operations. The promising results attained for the VR-based design testify that the technology is feasible and suitable to be intermingled with the visual design of art image. As a whole, the findings justify that the proposed approach is quite applicable in visual design of art image systems. In view of the significance of the research, the study can be extended to related domains of computer-based designing and visualization as well. [ABSTRACT FROM AUTHOR]

In order to improve the effect of 3D virtual product network display, this paper combines digital drive technology to analyze the virtual simulation algorithm and proposes a digital drive-based Hough transform clustering virtual image processing algorithm. Through the knowledge of clustering and generalized Hough transform, generalized Hough transform is applied to clustering. Moreover, this paper uses cluster analysis to determine the image characteristics of three-dimensional virtual products. In addition, this article combines the methods proposed in this article to construct a three-dimensional virtual product network display system. The research shows that the digital-driven 3D virtual product network display algorithm model proposed in this paper has a good 3D virtual display effect. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes. [ABSTRACT FROM AUTHOR]

Image sequence analysis is attracting significant attention at present, but its principles and techniques have rarely been applied to the field of sports biomechanics. As far as the technology of automatic recognition of joint points by computers is concerned, it is still in the experimental stage. The purpose of this paper is to study and analyze the sequence analysis and feature extraction of sports images based on cyclic neural network. This paper puts forward the basic concepts of sports image sequence analysis and feature extraction and analyzes the importance of sports in this context. As the experimental results demonstrates, the application rate of detecting human motion by using template matching technology detection is between 15% and 47%, while the accuracy of image sequence analysis method has increased from 17% to about 65%. Generally speaking, although the template matching technology detection method is more popular than the image sequence analysis method at the beginning, the popularity of the image sequence analysis method is significantly higher than that of the template matching technology detection method after time precipitation. Therefore, it is very important to study the sequence analysis and feature extraction of sports image based on cyclic neural network. [ABSTRACT FROM AUTHOR]

In order to improve students' overall subhealth behavior, teenagers' physical health problems have attracted more and more attention. The state clearly requires students to increase the number and frequency of exercise in school. In order to study the physical changes in the process of students' sports and the impact on their health caused by a sports injury, a student human motion feature image recognition based on a graph neural network is proposed in this paper. This paper combines image recognition technology with graphic neural network management and uses image recognition technology to detect and track targets. It also analyzes the health changes of students in sports and the influencing factors of physical subhealth in classroom learning. The results show that image recognition technology can accurately analyze the process of cervical spine injury and sports injury in students' classroom activities. It provides accurate experimental data for analyzing students' physical health and effective suggestions for promoting students' healthy development. Compared with the traditional image recognition and analysis results, the advantage of using a graph neural network to manage the detection and tracking results is that a graph neural network is used to manage the detection and tracking results, and the visual expression of students' physical health test data is completed. [ABSTRACT FROM AUTHOR]

At present, the segmentation method for the heart is one of the most difficult problems and a hot topic in medical imaging segmentation and image analysis. In order to achieve high-precision recognition of heart and blood vessel image, a single target auxiliary blood vessel image recognition algorithm, based on Markov Random Field (MRF) and ash fusion residual network information, is proposed in this paper. The corresponding features are extracted by the residual network depth, and the heart and blood vessels are segmented with high precision through MRF gray information to assist blood vessel image recognition. Thus, an algorithm combining Markov Random Field and Grayscale Information can complete such auxiliary recognition for single target ship image. The paper will also discuss auxiliary recognition techniques for identifying images of the heart and blood vessels. Added to that, it presents current medical image algorithms with a particular emphasis on cardiac image algorithms. Through a series of analysis, better algorithms are proposed. Thus, the research contents of a single target present the vascular image recognition algorithm. Therefore, a blood vessel image recognition algorithm is proposed for the research content of a single target. A series of validation experiments are conducted on the DRIVE and star datasets. The experimental results show that the method proposed in this paper has high segmentation accuracy, can achieve high-precision segmentation, and can achieve high-precision single target blood vessel image recognition. By observing and analyzing two-dimensional cardiac images, such as diabetic arterioles, hemorrhages, hard exudates, and so on, the difficulty of diagnosing cardiovascular disease characteristics can be reduced, and the diagnosis and treatment can be facilitated. [ABSTRACT FROM AUTHOR]

Digital image processing technology is widely used in production and life, and digital images play a pivotal role in the ever-changing technological development. Noise can affect the expression of image information. The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. Therefore, suppressing noise and improving the accuracy of edge detection are important aspects of image processing. To address these issues, this paper presents a new detection algorithm combined with information fusion based on the existing image edge detection techniques, and the algorithm is studied from two aspects of fuzzy radial basis fusion discrimination, in terms of preprocessing algorithm, comparing the denoising effect of mean and median filters with different template sizes on paper images with added noise, and selecting the improved median filter denoising, comparing different operator edge detection. The effect of image edge detection contour is finally selected as the 3 ∗ 3 Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle and labeled, and then, the original paper image is scanned line by line to segment the target image edge region. The image edge detection algorithm based on fuzzy radial basis fuser can not only speed up the image preprocessing, meet the real-time detection, and reduce the amount of data processed by the upper computer but also can accurately identify five image edge problems including folds and cracks, which has good application prospects. [ABSTRACT FROM AUTHOR]

Liver fibrosis is a common liver disease that seriously endangers human health. Liver biopsy is the gold standard for diagnosing liver fibrosis, but its clinical use is limited due to its invasive nature. Ultrasound image examination is a widely used liver fibrosis examination method. Clinicians can diagnose the severity of liver fibrosis according to their own experience by observing the roughness of the texture of the ultrasound image, and this method is highly subjective. Under the premise that artificial intelligence technology is widely used in medical image analysis, this paper uses convolutional neural network analysis to extract the characteristics of ultrasound images of liver fibrosis and then classify the degree of liver fibrosis. Using neural network for image classification can avoid the subjectivity of manual classification and improve the accuracy of judging the degree of liver fibrosis, so as to complete the prevention and treatment of liver fibrosis. Therefore, the following work is done in this paper: (1) the research background, research significance, research status at home and abroad, and the impact of the development of medical imaging on the diagnosis of liver fibrosis are introduced; (2) the related technologies of deep learning and deep convolutional network are introduced, and the indicators of liver fibrosis degree assessment are constructed by using ultrasonic image extraction features; (3) using the collected liver fibrosis dataset to conduct model evaluation experiments, four classic CNN models are selected to compare and analyze the recognition rate. The experiments show that the GoogLeNet model has the best classification and recognition effect. [ABSTRACT FROM AUTHOR]

Medical image analysis technology based on deep learning has played an important role in computer-aided disease diagnosis and treatment. Classification accuracy has always been the primary goal pursued by researchers. However, the image transmission process also faces the problems of limited wireless ad-hoc network (WAN) bandwidth and increased security risks. Moreover, when user data are exposed to unauthorized users, platforms can easily leak personal privacy. Aiming at the abovementioned problems, a system model and an access control scheme for the collaborative analysis of the diagnosis of diabetic retinopathy (DR) are constructed in this paper. The system model includes two stages of data cleaning and lesion classification. In the data cleaning phase, the private cloud writes the model obtained after training into the blockchain, and other private clouds use the best-performing model on the chain to identify the image quality when cleaning data and pass the high-quality image to the lesion classification model for use. In the lesion classification stage, each private cloud trains the classification model separately; uploads its own model parameters to the public cloud for aggregation to obtain a global model; and then sends the global model to each private cloud to achieve collaborative learning, reduce the amount of data transmission, and protect personal privacy. Access control schemes include improved role-based access control (RAC) used within the private cloud and blockchain-based access control used during the interaction between the private cloud and the public cloud program (BAC). RAC grants both functional rights and data access rights to roles and takes into account object attributes for fine-grained level control. Based on certificateless public-key encryption technology and blockchain technology, BAC can realize the identity authentication and authority identification of the private cloud while requesting the transmission of model parameters from the private cloud to the public cloud and protect the security of the identity, authority, and model parameters of the private cloud to achieve the effect of lightweight access control. In the experimental part, two retinal datasets are used for DR classification analysis. The results show that data cleaning can effectively remove low-quality images and improve the accuracy of early lesion classification for doctors, with an accuracy rate of 90.2%. [ABSTRACT FROM AUTHOR]

Under the development trend of today's society, any brand attaches great importance to its outer packaging, and the research and analysis of the design effect of outer packaging has always troubled many brands. The purpose of this paper is to conduct a comprehensive study on the integration of artistic visual elements in the design effect of product packaging through the use of pattern recognition technology. Therefore, this paper proposes an improvement to the feature analysis algorithm of image recognition in pattern recognition technology and understands the specific principle of pattern recognition and improves its specific recognition method. At the same time, in the experiment and analysis part, this paper conducts research experiments on the improved algorithm and explores the performance of the algorithm and the adaptability of the algorithm in the actual packaging design recognition process. The experimental results of this paper show that the improved pattern recognition improves the accuracy of text recognition in packaging by 18% and the accuracy of image noise point recognition by nearly 30%. It can be detected very efficiently in the artistic visual fusion process of packaging design. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the key technologies for SAR image interpretation. This paper proposes a SAR target recognition method based on collaborative representation-based classification (CRC). The collaborative coding adopts the global dictionary constructed by training samples of all categories to optimally reconstruct the test samples and determines the target category according to the reconstruction error of each category. Compared with the sparse representation methods, the collaborative representation strategy can improve the representation ability of a small number of training samples for test samples. For SAR target recognition, the resources of training samples are very limited. Therefore, the collaborative representation is more suitable. Based on the MSTAR dataset, the experiments are carried out under a variety of conditions and the proposed method is compared with other classifiers. Experimental results show that the proposed method can achieve superior recognition performance under the standard operating condition (SOC), configuration variances, depression angle variances, and a small number of training samples, which proves its effectiveness. [ABSTRACT FROM AUTHOR]

Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model's recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively. [ABSTRACT FROM AUTHOR]

In order to promote the simultaneous development of art creation and modern information technology, this paper studies the application of digital technology in the improvement of art creation. Moreover, this paper combines image analysis technology and computer simulation technology to improve point cloud digital technology and build a human-computer interaction system for art creation based on digital technology. The system uses the visual computer to communicate, gives instructions through actions, and uses the computer's visual dynamic tracking technology to make the computer recognize the instructions and information sent by the user so as to provide visual feedback. In addition, this visual interactive art creation application uses the real-time nature of the camera to capture real images of interaction between the environment and the work and then interacts the captured images with virtual bubbles. Finally, this paper studies the method proposed in this paper through case analysis. The research results show that digital technology can effectively promote the development of art creation. [ABSTRACT FROM AUTHOR]

In recent years, with the rise of artificial intelligence, deep neural network models have been used in various image recognition researches. Land desertification is a major environmental problem facing the world at present, and how to do a good job in dynamic monitoring is particularly important. For remote sensing images, this paper constructs a GA-PSO-BP analysis model based on BP neural network, genetic algorithm, and particle swarm algorithm and compares the classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP; GA-PSO-BP was selected for dynamic analysis of desertification images, and the results showed the following: (1) By comparing the regional classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP, the GA-PSO-BP neural network remote sensing image classification method proposed in this paper is simple and easy to operate. Compared with traditional remote sensing image classification methods and traditional neural network classification methods, the classification accuracy of remote sensing effects is improved. (2) Carrying out desertification analysis on remote sensing images of Horqin area, from 2010 to 2015, the desertified land area in the test area increased by 1.56 km2; from 2015 to 2020, the desertified land area in the test area decreased by 1.131 km2, and the desertified land in the test area from 2010 to 2020 showed a trend of increasing first and then decreasing, which is consistent with the actual situation. The GA-PSO-BP remote sensing image classification model has a good performance portability. [ABSTRACT FROM AUTHOR]

Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature. [ABSTRACT FROM AUTHOR]

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB. [ABSTRACT FROM AUTHOR]

With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons. In this paper, three different illuminations, including front lighting, normal lighting, and back lighting, are first applied to citrus based on the computer vision technology. Secondly, the image data of the fruits, fruit stems, and leaves of the citrus are collected. The color component distributions of citrus based on different color models are analyzed according to the corresponding characteristic values, and an exploratory data analysis process for the image data of citrus is established. In addition, 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. The results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images. [ABSTRACT FROM AUTHOR]

Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around. [ABSTRACT FROM AUTHOR]

With the aggravation of human sub-health problems, people's demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people's diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods. [ABSTRACT FROM AUTHOR]

Sports video is loved by the audience because of its unique charm, so it has high research value and application value to analyze and study the video data of competition. Based on the background of football match, this paper studies the football detection and tracking algorithm in football game video and analyzes the real-time image of real-time mobile devices in sports video augmented reality. Firstly, the image is preprocessed by image graying, image denoising, image binarization, and so on. Secondly, Hough transform is used to locate and detect football, and according to the characteristics of football, Hough transform is improved. Based on the good performance of SIFT algorithm in feature matching, a football tracking algorithm based on SIFT feature matching is proposed, which matches the detected football with the sample football. The simulation results show that the improved Hough transform can effectively detect football and has good antijamming performance. And the designed football tracking algorithm based on SIFT feature matching can accurately track the football trajectory; therefore, the football detection and tracking algorithm designed in this paper is suitable for real-time football monitoring and tracking. [ABSTRACT FROM AUTHOR]

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks. [ABSTRACT FROM AUTHOR]

With people's attention to physical health and health preservation, more and more people practice Taijiquan to achieve the purpose of physical fitness and self-cultivation. However, many people do not fully understand the principles of Taijiquan in the early stage of practicing Taijiquan, rely on imitation to practice, and ignore the action essentials and characteristics of Taijiquan, resulting in joint pain. At the same time, many beginners ignore the causes of these situations, resulting in joint damage. Therefore, this paper proposes the research on the joint injury of Taijiquan based on computer image analysis and carries out the dependent research and analysis through the joint image processing model based on the improved Robert's edge detection algorithm. The experimental results show that the action cycle time of wrong action is longer than the total time of correct action due to the increase of action displacement, and the long displacement distance increases the pressure on the knee joint and improves the probability of knee joint damage. In addition, there are obvious differences between wrong action and correct action in knee adduction and abduction, flexion and extension, internal rotation, and external angle. Wrong action knee joint movement does not conform to the natural movement law of human body, which increases the pressure or tension to the knee joint and surrounding related tissues and improves the probability of knee joint damage. [ABSTRACT FROM AUTHOR]

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature's extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%. [ABSTRACT FROM AUTHOR]

Image classification and recognition has a very wide range of applications in computer vision, which involves many fields, such as image retrieval, image analysis, and robot positioning. Especially with the rise of brain science and cognitive science research, as well as the increasing diversification of imaging means, three-dimensional image data mainly based on magnetic resonance image plays an increasingly important role in image classification and recognition, especially in medical image classification and recognition. However, due to the high dimensional characteristics of human magnetic resonance images, human readability is reduced. Therefore, classification and recognition of 3-dimensional images is still a challenge. In order to better extract local features from images and effectively use their spatial information, this paper improved the "feature bag" and "spatial pyramid matching" algorithms on the basis of 3D feature extraction algorithm and proposed an image classification framework based on 3D feature extraction algorithm. Firstly, the multiresolution "3D spatial pyramid" algorithm, the multiscale image segmentation and image representation method, and the SVM classifier and feature fusion method are described. Secondly, the gender information contained in the magnetic resonance images is classified and recognized on the three databases selected in the experiment. Experimental results show that this method can effectively utilize the spatial information of three-dimensional images and achieve satisfactory results in the classification and recognition of human magnetic resonance images. [ABSTRACT FROM AUTHOR]

Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set. [ABSTRACT FROM AUTHOR]

Nanotechnology is an alternative material manufacturing technology, and the research field involves a broad field of modern technology, especially in the field of high-end manufacturing. Nanotechnology is a technique for studying the properties and applications of materials with structural dimensions in the range of 1 nanometer to 100 nanometers. Medical imaging refers to the technology and processing process of obtaining internal tissue images of the human body or a certain part of the human body in a noninvasive way for medical treatment or medical research; the acquired image can be further processed to restore the original image that was not clear enough to highlight some characteristic information in the image. The appearance of medical imaging has promoted the development of the medical field. The purpose of this paper is to study the application of nanoscale microparticle technology in medical imaging diagnosis and treatment. It is expected that the combination of nanotechnology and medical imaging will improve the accuracy of medical imaging diagnosis. This paper mainly introduces the general methods and technologies of technical development of medical imaging instruments and briefly shows the process of data and image analysis. The experimental results in this paper show that when other conditions remain unchanged, the image quality will change when the quality factor of the image changes. When the image quality is 60%, the image transmission rate is about 5.5FPS, which is relatively stable and can be used in the medical field. [ABSTRACT FROM AUTHOR]

Computer vision technology began to affect the development of football. There is increasingly high-tech in football broadcast technology, and many application tools have emerged in the field of football broadcast video analysis. The purpose of this paper is to study the improvement of target tracking algorithm for football broadcast video and to study the intelligent optimization algorithm of 3D tracking technology in football player moving image analysis. This paper proposes to select four models of YOLOv5 to perform target detection experiments in football broadcast videos and analyzes the principle of the Deep SORT multitarget tracking algorithm. At the same time, it is based on the 3D tracking and 3D pose estimation of players based on cross-view correlation matching, and to measure the comprehensive performance of the tracker in the football scene, experiments are carried out on the accuracy and speed of the tracker under the football datasets of four different scenes. The experimental results in this paper show that the MOTA values corresponding to the 3D tracking results and 2D projection results obtained in the campus dataset are only 50 and 56.2. This is much lower than the tracking performance when based on other similarity matrices. The MOTA value of the obtained tracking result (92.6) is very close and significantly outperforms other methods. CCOT performs better on datasets 28, 29, 31, and 32, ECO stands out on dataset 38, and Siamese also performs well on datasets 22 and 36. [ABSTRACT FROM AUTHOR]

Malignant melanoma is considered one of the deadliest skin diseases if ignored without treatment. The mortality rate caused by melanoma is more than two times that of other skin malignancy diseases. These facts encourage computer scientists to find automated methods to discover skin cancers. Nowadays, the analysis of skin images is widely used by assistant physicians to discover the first stage of the disease automatically. One of the challenges the computer science researchers faced when developing such a system is the un-clarity of the existing images, such as noise like shadows, low contrast, hairs, and specular reflections, which complicates detecting the skin lesions in that images. This paper proposes the solution to the problem mentioned earlier using the active contour method. Still, seed selection in the dynamic contour method has the main drawback of where it should start the segmentation process. This paper uses Gaussian filter-based maximum entropy and morphological processing methods to find automatic seed points for active contour. By incorporating this, it can segment the lesion from dermoscopic images automatically. Our proposed methodology tested quantitative and qualitative measures on standard dataset dermis and used to test the proposed method's reliability which shows encouraging results. [ABSTRACT FROM AUTHOR]

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability. [ABSTRACT FROM AUTHOR]

Cartoon rendering of images is a challenging nonphotorealistic image rendering task, which aims to transform real photos into cartoon-style nonphotorealistic images while preserving the semantic content and texture details of the original photos. Based on the understanding of the characteristics of cartoon images and analysis of the defects of established approaches, this paper improves existing methods. The convolutional neural networks' powerful image processing ability and attention mechanism are utilized to get more fine-grained image features, making the result of rendering more realistic. This paper mainly studies how neural networks can better process cartoon pictures, adjust parameters, and choose training methods. Furthermore, the article proposes a new solution of cartoon-style image rendering based on deep learning. The actual test results of real original images have shown that the model is suitable for cartoon-style image rendering. [ABSTRACT FROM AUTHOR]

As one of the most common imaging screening techniques for spinal injuries, MRI is of great significance for the pretreatment examination of patients with spinal injuries. With rapid iterative update of imaging technology, imaging techniques such as diffusion weighted magnetic resonance imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy are frequently used in the clinical diagnosis of spinal injuries. Multimodal medical image fusion technology can obtain richer lesion information by combining medical images in multiple modalities. Aiming at the two modalities of DCE-MRI and DWI images under MRI images of spinal injuries, by fusing the image data under the two modalities, more abundant lesion information can be obtained to diagnose spinal injuries. The research content includes the following: (1) A registration study based on DCE-MRI and DWI image data. To improve registration accuracy, a registration method is used, and VGG-16 network structure is selected as the basic registration network structure. An iterative VGG-16 network framework is proposed to realize the registration of DWI and DCE-MRI images. The experimental results show that the iterative VGG-16 network structure is more suitable for the registration of DWI and DCE-MRI image data. (2) Based on the fusion research of DCE-MRI and DWI image data. For the registered DCE-MRI and DWI images, this paper uses a fusion method combining feature level and decision level to classify spine images. The simple classifier decision tree, SVM, and KNN were used to predict the damage diagnosis classification of DCE-MRI and DWI images, respectively. By comparing and analyzing the classification results of the experiments, the performance of multimodal image fusion in the auxiliary diagnosis of spinal injuries was evaluated. [ABSTRACT FROM AUTHOR]

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance. [ABSTRACT FROM AUTHOR]

The heart is one of the most important organs of the human body, but in recent years heart disease has become one of the human health killers and this paper explores endomyocardial fibrosis, which is a common cardiomyopathy, commonly seen in infants and children, and refers to a diffuse elastic fibrous disease of the endocardium. The purpose of this paper is to explore the diagnostic imaging analysis and care of patients with endocardial heart machine fibrosis using wireless network intelligent medical technology, aiming to provide a new power basis for the treatment of the disease in related patients. This paper proposes a new endocardial segmentation algorithm that aims to process image information using image features, intervene in image noise reduction and smoothing, etc., and use image grayscale values to confirm cardiac cavity grayscale values as a basis for physicians to make certain judgments for the diagnosis of patients with endocardial machine fibrosis. The experimental results show that the atrial fibrillation group is distinctly higher compared to the sinus rhythm group, with values remaining between 25 and 39, which is a significant advantage compared to other methods. [ABSTRACT FROM AUTHOR]

As an important branch of multisensor information fusion, image fusion is widely used in various fields. As a hot research technology, virtual reality technology can bring different levels of experience to image fusion. At the same time, with the development of existing image processing software, it is conducive to the further analysis and processing of images. In today's market, image processing technology still faces many problems, and with the advancement of technology, virtual technology is widely used in various fields, so combining virtual technology with images is conducive to improving image processing technology. This article mainly introduces the image fusion algorithm and its application research based on virtual reality technology and Nuke software. This paper first proposes a picture fusion model and a picture fusion system through the analysis of virtual technology and Nuke software and, on this basis, proposes a particle algorithm and a picture edge algorithm. Secondly, the optimal fusion of images is studied on Nuke software, and finally the experimental results are analyzed through image fusion algorithm. Studies have shown that the best image fusion greatly improves the security and privacy of the image, and the difficulty of cracking is as high as 80%. The data in the experimental analysis of the graphic fusion algorithm shows that the execution efficiency and time consumption of the algorithm are greatly shortened, and the time consumption is greatly reduced. The rate is reduced by about 50%, and a good image fusion effect has been achieved. [ABSTRACT FROM AUTHOR]

Multiagent technology, as a conceptual model commonly used in intelligent systems, has now had a lot of related research, and it is also widely used in sports training simulation systems. This paper mainly studies the development of the sports training simulation system based on agent technology. This paper proposes the motion perception system of multiagent technology in sports training. In the research, this paper introduces the related concepts and methods of agent in detail. In order to better simulate the changes of human joints during exercise with the system, this article uses a variety of comparison methods. Among them, the experiment of the imaging analysis module is carried out through the simulation of the simulation system. The experimental results show that the role of multiagent in the simulation system is huge. In linear motion, the accuracy of joint extraction has reached more than 95%. The extraction of joints in the curve movement reached more than 80%, indicating that it can not only complete the relevant processing adaptively but also predict and correct the exercise process of the trainer. Therefore, in the research of sports training simulation system, more attention should be paid to the research of curve motion. [ABSTRACT FROM AUTHOR]

In this paper, the signal of athletic sports video image frames is processed and studied according to the technology of big data. The sports video image-multiprocessing technology achieves interference-free research and analysis of sports technology and can meet multiple visual needs of sports technology analysis and evaluation through key technologies such as split-screen synchronous comparison, superimposed synchronous comparison, and video trajectory tracking. The sports video image-processing technology realizes the rapid extraction of key technical parameters of the sports scene, the panoramic map technology of sports video images, the split-lane calibration technology, and the development of special video image analysis software that is innovative in the field of athletics research. An image-blending approach is proposed to alleviate the problem of simple and complex background data imbalance, while enhancing the generalization ability of the network trained using small-scale datasets. Local detail features of the target are introduced in the online-tracking process by an efficient block-filter network. Moreover, online hard-sample learning is utilized to avoid the interference of similar objects to the tracker, thus improving the overall tracking performance. For the feature extraction problem of fuzzy videos, this paper proposes a fuzzy kernel extraction scheme based on the low-rank theory. The scheme fuses multiple fuzzy kernels of keyframe images by low-rank decomposition and then deblurs the video. Next, a double-detection mechanism is used to detect tampering points on the blurred video frames. Finally, the video-tampering points are located, and the specific way of video tampering is determined. Experiments on two public video databases and self-recorded videos show that the method is robust in fuzzy video forgery detection, and the efficiency of fuzzy video detection is improved compared to traditional video forgery detection methods. [ABSTRACT FROM AUTHOR]

Abdominal B-ultrasound images of intrauterine pregnancy tissue residues were analyzed to discuss their diagnostic value. With the rapid development of computer technology and medical imaging technology, doctors are also faced with more and more medical image diagnosis tasks, and computer-aided diagnosis systems are especially important in order to reduce the work pressure of doctors. In recent years, deep learning has made rapid development and achieved great breakthroughs in various fields. In medical-aided diagnostic systems, deep learning has greatly improved the diagnostic efficiency, but there are no mature research results for abdominal B-ultrasound image recognition of intrauterine pregnancy tissue residues. Therefore, the study of liver ultrasound image classification based on deep learning has important practical application value. In this paper, we propose to give a CNN model optimization method based on grid search. Compared with the conventional CNN model design, this method saves time and effort by eliminating the need to manually adjust parameters based on experience and has an accuracy of more than 92% in classifying abdominal B-ultrasound images of intrauterine pregnancy tissue residues. The diagnosis of intrauterine pregnancy tissue residues by abdominal B-ultrasound can effectively improve the diagnosis and provide important reference for patients to receive treatment, which has high diagnostic value. [ABSTRACT FROM AUTHOR]

This paper uses the GIS technology to collect location and geographic information of the South Atlantic ecological environment and then uses the remote sensing image technology to extract and analyse the collected information to provide a strong basis for maintaining a healthy marine ecological environment. Based on multitemporal remote sensing images, this paper constructs an ecological vulnerability evaluation model for marine by coupling several ecological indicators, analyses the status of marine ecological environment and its dynamic changes, explores the driving factors of its changes, further analyses the relationship between ecological vulnerability, land use changes, and landscape patterns, and proposes targeted ecological protection countermeasures for marine ecological environment. This paper analyses and studies the quality control mechanism of marine environment monitoring data, the construction of marine environment evaluation model, and the automation of evaluation and early warning response based on the application of GIS, with the support of marine environment evaluation and early warning business, to enhance the availability and integrity of marine environment monitoring data, improve the execution efficiency of marine environment evaluation business and early warning response, reduce the risk of marine environment emergencies, and realize the integration and intelligence of marine environment evaluation and early warning, to improve the level of Informa ionization and management of marine environment evaluation business in marine management departments. The operational watershed ecological environment monitoring service system built in this paper, which integrates GIS, RS, GPS, data warehouse, and other information technologies, has the advantages of high accuracy of information extraction, convenient data management, good portability, and strong operability, which can provide a scientific basis for the unified scheduling management of the marine environment, ecological environment evaluation, and sustainable development planning of the economy, and it is of great theoretical and practical significance and can be used in other watersheds. Meanwhile, the unique environment of the ocean has endowed the marine microorganism with affluent diversity, which can display a variety of bioactivities. In this study, thirty-two seawater and sediment samples were collected from different depths within the scope of 5 m to 3203 m at 18 sampling sites in the South Atlantic Ocean. 1017 culturable strains were obtained after isolation and identification. The tip culture method was used to screen strains with antifungal and antiaflatoxigenic activities. The tip culture results showed that a total of thirty-one marine strains were confirmed to have significant antagonism against aflatoxin production with an inhibition ratio exceeding 80%, including 11 strains with a mycelial growth inhibition ratio exceeding 80% simultaneously. The variability of culturable bacterial distribution and diversity were studied by phylogenetic analysis of 16S rRNA sequences. Phylogenetic analysis of 16S rRNA sequences revealed a significant diversity of culturable strains. A total of 31 strains clustered into 13 genera: Marinobacter sp., Pseudomonas sp., Alteromonas sp., Idiomarina sp., Halomonas sp., Alcanivorax sp., Thalassospira sp., Jannaschia sp., Stakelama sp., Erythrobacter sp., Chengkuizengella sp., Bacillus sp., and Microbacterium sp. The 31 strains with antifungal and antiaflatoxigenic activities were dominated by populations of strains belonging to the genus Thalassospira (19.35%). To the best of our knowledge, this is also the first report to analyse the diversity of culturable bacteria with antifungal and antiaflatoxigenic activities in the South Atlantic Ocean from three perspectives: different media, different sampling stations, and different sampling depths. [ABSTRACT FROM AUTHOR]

The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image target recognition technology is aimed at automatically determining the presence or absence of target information from the input SAR image and improving the efficiency and accuracy of SAR image interpretation. Based on big data analysis, dirty data is removed, clean data is returned, and standardized processing of SAR image data is realized. At the same time, by establishing a statistical model of coherent speckles, the convolutional autoencoder is used to denoise the SAR image. Finally, the network model modified by softmax cross-entropy loss and Fisher loss is used for automatic target recognition. Based on the MSTAR data set, two scene graphs containing the target synthesized by the background image and the target slice are used for experiments. Several comparative experiments have verified the effectiveness of the classification and recognition model in this paper. [ABSTRACT FROM AUTHOR]

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy. [ABSTRACT FROM AUTHOR]

This paper combines optimized CSMRI algorithm (CS) and magnetic resonance imaging (MRI) to shorten the scanning time of MRI image data and improve the imaging quality. At the same time, the paper applies functional magnetic resonance imaging (BOLD-fMRI) based on the principle of blood oxygen level dependence to explore the application value of the nerve function reconstruction therapy system for the rehabilitation of active and passive motor functions in patients with acute cerebral infarction. Methods. In this paper, 20 patients with acute cerebral infarction were included. The random drawing method was used to divide them into active group and passive group, each with 10 cases. Both groups were treated with conventional medication and acupuncture. The active group used the active mode of the nerve function reconstruction treatment system to guide the patients' limb active exercise; all training in the passive group is provided by the nerve function reconstruction treatment system to passively exercise the patients' limbs; both groups undergo BOLD-fMRI examination before treatment and after 2 weeks of treatment and observe the activated parts of the brain functional area and corresponding parts of the two groups before and after treatment. We observe the activation volume and, at the same time, the ADL score. Results. After treatment, the activation volume and ADL scores of brain functional areas in the two groups were significantly improved compared with those before treatment, and the difference was statistically significant (P < 0.05). Conclusion. The combination of optimized CSMRI algorithm (CS) and magnetic resonance imaging (MRI) can be used to evaluate the early rehabilitation efficacy of patients with acute cerebral infarction and has certain guiding value for clinical treatment. [ABSTRACT FROM AUTHOR]

Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image. [ABSTRACT FROM AUTHOR]

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%. [ABSTRACT FROM AUTHOR]

Wear particle image analysis is an effective method to detect wear condition of mechanical devices. However, the recognition accuracy and recognition efficiency for online wear particle automatic recognition are always mutual restricted because the online wear particle images have almost no texture information and lack clarity. Especially for confusing fatigue wear particles and sliding wear particles, the online recognition is a challenging task. Based on this requirement, a super-resolution reconstruct technique and partial hierarchical convolutional neural network, SR-PHnet, is proposed to classify wear particles in one step. The structure of this network is composed by three modules, one is super-resolution layer module, the second is convolutional neural network classifier module, and the third is support vector machine (SVM) classifier module. The classification result of the second module is partial input to the third module for precision classification of fatigue and sliding particles. In addition, a new feature of radial edge factor (REF) is put forward to target fatigue and sliding wear particles. The test result shows that the new feature has the capability to distinguish fatigue and sliding particles well and time saving. The comparison experiments of the convolution neural network (CNN) method, support vector machine method (SVM) with and without REF feature, and integrated model of back-propagation (BP) and CNN are produced. The comparison results show that the online recognition speed and online recognition rate of the proposed SR-PHnet model in this paper are both improved markedly, especially for fatigue and sliding wear particles. [ABSTRACT FROM AUTHOR]

Incomplete remote sensing image observation data require proper processing to restore the original data. Nevertheless, the lack of information, due to noise and dead pixels, along with the quality of the acquired images that may be severely degraded, limits the accuracy of later processing. Therefore, this paper proposes a remote sensing image restoration scheme that employs the low-rank structure of the Bayesian probability tensor latent space and a new tensor completion method, which has strong robustness for the model selection process of the following remote sensing image data analysis. Considering the data space, we incorporate an adaptive nuclear norm regularization into the processing of latent factors that performs singular value decomposition on a more accurate scale. The proposed model adopts two algorithms: Augmented Lagrange Multiplier (ALM) and Alternating Least Squares (ALS). Several synthetic and real-data-based experiments illustrate the inherent ability of our method in restoring original information and preventing overfitting problems, even when a vast number of items are missing. In addition, the combination of adaptive nuclear norm regularization and tensor decomposition is stable, settling a high sensitivity of rank selection and low computational efficiency in traditional tensor reconstruction methods. [ABSTRACT FROM AUTHOR]