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Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

This document is a special issue of the Journal of Intelligent Information Systems, focusing on the selected papers from the International Symposium on Methodologies for Intelligent Systems (ISMIS 2022). The symposium, held in Cosenza, Italy, showcased research on various topics related to artificial intelligence, including decision support, knowledge representation, machine learning, computer vision, and more. The special issue includes eleven papers that have undergone rigorous peer-reviewing and cover a wide range of research topics, such as deep learning, anomaly detection, malware detection, sentiment classification, and healthcare professionals' burnout. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]

Computer vision is one of the artificial intelligence's most challenging fields, enabling computers to interpret, analyse and derive meaningful information from the visual world. There are various utilizations of computer vision algorithms, and most of them, from simpler to more complicated, have an object and shape recognition in common. Traditional pen and paper tests are designed in a pre-established format and consist of numerous basic shapes, which designate the important parts of the test itself. With that in mind, many computer vision applications regarding pen and paper tests arise as an opportunity. Massive courses and large schooling organizations mostly conduct their exams in paper format and assess them manually, which imposes a significant burden on the teaching staff. Any kind of automatization that will facilitate the grading process is highly desirable. Hence, an automated answer recognition system in assessment was developed to mitigate the problems above. The system uses images of scanned test pages obtained from the test scanning process and performs the necessary image manipulation steps to increase target recognition accuracy. Further, it manages to identify regions of interest containing multiple-choice questions and contours. Finally, the system verifies obtained results using the knowledge of the whereabouts of the test template regions of interest. [ABSTRACT FROM AUTHOR]

Computer-vision-based gaze estimation refers to techniques that estimate gaze direction directly from video recordings of the eyes or face without the need for an eye tracker. Although many such methods exist, their validation is often found in the technical literature (e.g., computer science conference papers). We aimed to (1) identify which computer-vision-based gaze estimation methods are usable by the average researcher in fields such as psychology or education, and (2) evaluate these methods. We searched for methods that do not require calibration and have clear documentation. Two toolkits, OpenFace and OpenGaze, were found to fulfill these criteria. First, we present an experiment where adult participants fixated on nine stimulus points on a computer screen. We filmed their face with a camera and processed the recorded videos with OpenFace and OpenGaze. We conclude that OpenGaze is accurate and precise enough to be used in screen-based experiments with stimuli separated by at least 11 degrees of gaze angle. OpenFace was not sufficiently accurate for such situations but can potentially be used in sparser environments. We then examined whether OpenFace could be used with horizontally separated stimuli in a sparse environment with infant participants. We compared dwell measures based on OpenFace estimates to the same measures based on manual coding. We conclude that OpenFace gaze estimates may potentially be used with measures such as relative total dwell time to sparse, horizontally separated areas of interest, but should not be used to draw conclusions about measures such as dwell duration. [ABSTRACT FROM AUTHOR]

The fourth paper which is about "Emotional AI and Crime" takes the argument into the key area of how good or bad are AI techniques that are designed for facial and related recognition. To an extent, the focus on AI is wider than what might find in any discussion of AI in the narrower technical field for context is all important to see urban AI in context. Artificial intelligence (AI) emerged alongside the development of the digital computer more than 80 years ago during the second world war. [Extracted from the article]

This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper. [ABSTRACT FROM AUTHOR]

Experimental results on five public gene expression profile datasets verify that the proposed algorithm outperforms other methods in terms of diversity, distribution stability, and quality of generated samples. The fifth paper by I Tingyu Ye i et al. on "Artificial bee colony algorithm with an adaptive search manner and dimension perturbation" proposes a modified artificial bee colony (ABC) algorithm with an adaptive search manner and dimension perturbation (ASDABC). The tenth paper by I Fei Han i et al. on "Gene-CWGAN: A Data Enhancement Method for Gene Expression Profile Based on Improved CWGAN-GP" tackles the problem of high dimension and small sample size of gene expression profile data classification. [Extracted from the article]

Defect detection systems based on machine vision have been widely used as an essential part of intelligent manufacturing systems. However, in traditional object detection methods that rely on images as input, differences in defect areas, blurred images, and complex background interference can seriously impair detection accuracy. To meet these challenges, this paper proposed a dual-path neural network based on shearlet transform (STDPNet) by taking advantage of shearlet transform in multi-scale analysis and combining it with the improved object detection algorithm proposed in this paper. First, images are multi-scale and multi-directional decomposed with shearlet transform, and multi-directional sub-band information is input to the detection network instead of image information. Then, this paper proposed a dual-path object detection network for the differences between different frequency bands and introduced a transfer learning strategy between paths to improve the model performance. Finally, the training results on the NEU surface defect public dataset show that the mean average precision of STDPNet achieves 86.81% at a detection speed of 44.45 f/s, which exceeds that of Faster R-CNN by 12%. Experiments on different datasets prove that the accuracy is significantly superior to other models, and the proposed method is more advantageous compared to other models in large, fuzzy, and indistinguishable defect types. [ABSTRACT FROM AUTHOR]

This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset. Instead of generic and commonly used representations trained on the ImageNet classification task, our approach employs a combination of networks trained for specialized tasks such as scene recognition, person activity classification, and attribute prediction. We also present a method for localizing phrases from candidate answers in order to provide spatial support for feature extraction. We map each of these features, together with candidate answers, to a joint embedding space through normalized canonical correlation analysis (nCCA). Finally, we solve an optimization problem to learn to combine scores from nCCA models trained on multiple cues to select the best answer. Extensive experimental results show a significant improvement over the previous state of the art and confirm that answering questions from a wide range of types benefits from examining a variety of image cues and carefully choosing the spatial support for feature extraction. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet + + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness. [ABSTRACT FROM AUTHOR]

Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets. [ABSTRACT FROM AUTHOR]

Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models. [ABSTRACT FROM AUTHOR]

Image segmentation is a key problem in the field of machine vision. Its core goal is to separate the target and background in the region of interest from the image and directly affect the accuracy of subsequent operations such as target recognition and image understanding. In the past decades, there have been many good image segmentation algorithms. In recent years, the deep learning method represented by deep learning has made great progress in the field of image segmentation. In this paper, some commonly used image segmentation algorithms based on machine learning were reviewed, and their theoretical and experimental studies were carried out. In this paper, the application prospect of machine learning in image segmentation was prospected. The existing image segmentation methods are mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on specific theories. In recent years, with the rapid progress of computer vision technology, the requirements for the accuracy of object image edge information segmentation have become increasingly high. The main reason for image segmentation is to better obtain object information. However, due to interference conditions such as lighting and noise, image blurry edge information segmentation has become the most difficult point in the development of computer vision technology. In the comparative experiment of algorithms, the results showed that in the training set, the response time of Deep Neural Network (DNN) algorithm, Cluster Analysis (CA) algorithm, and Support Vector Machine (SVM) algorithm was 13.72 s, 16.88 s and 17.29 s when the number of samples was 150. In the test set, when the sample number was 50, the recognition rate of DNN algorithm was 93.7%; the recognition rate of CA algorithm was 87.9%; the recognition rate of SVM algorithm was 84.3%. Therefore, the research of image fuzzy edge information segmentation based on computer vision and machine learning is essential. [ABSTRACT FROM AUTHOR]

The document is an editorial message from the guest editors of the Computational Visual Media (CVM) 2024 Special Issue. The CVM conference series aims to facilitate the exchange of research ideas and computational methodologies related to visual media. The 12th international conference, CVM 2024, was held in New Zealand and received significant attention from researchers worldwide. The special issue includes 10 outstanding papers covering various topics such as 3D fabrication, image processing, and music generation. The editors express gratitude to the authors and reviewers for their contributions. [Extracted from the article]

The text is a preface to the Journal of Computer Science & Technology's special section on the Computational Visual Media (CVM) conference series. The CVM conference serves as an international platform for the exchange of research ideas and computational methodologies related to visual media. The conference encourages collaboration across disciplines such as computer graphics, computer vision, machine learning, and more. The 12th international conference in the series, CVM 2024, was held in Wellington, New Zealand, and attracted attention from researchers worldwide. The special section includes six outstanding papers covering various topics, as well as an invited survey paper. The editors express gratitude to the authors and reviewers for their contributions. The preface also provides brief biographies of the leading editor and guest editors. [Extracted from the article]

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

The recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require first decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifier, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifier largely compatible with the state-of-the-art spatial domain PointGrid classifier. However, the available Compressed Domain PC Classifier presents strong limitations by imposing a single, specific input size which is associated to specific JPEG Pleno PCC configurations; this limits the compression performance as these configurations are not ideal for all PCs due to their different characteristics, notably density. To overcome these limitations, this paper proposes the first Adaptive Compressed Domain PC Classifier solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using different coding configurations, now maximizing the compression efficiency. Experimental results show that the novel Adaptive Compressed Domain PC Classifier allows JPEG PCC to achieve better compression performance by not imposing a single, specific coding configuration for all PCs, regardless of its different characteristics. Moreover, the added adaptability power can achieve slightly better PC classification performance than the previous Compressed Domain PC Classifier and largely better PC classification performance (and lower number of weights) than the PointGrid PC classifier working in the decompressed domain. [ABSTRACT FROM AUTHOR]

Hand tracking is relevant to such a variety of applications including human-robot interaction (HRI), human-computer interaction (HCI), virtual reality (VR), and augmented reality (AR). Accurate and robust hand tracking however is challenging due to the intricacies of dynamic motion within small space and the complex interactions with nearby objects, coupled with the hurdles in real-time hand mesh reconstruction. In this paper, we conduct a comprehensive examination and analysis of existing hand tracking technologies. Through the review of major works in the literature, we have discovered numerous studies employing a diverse array of sensors, leading us to propose their categorization into seven types: vision, soft wearable, encoder, magnetic, inertial measurement unit (IMU), electromyography (EMG), and the fusion of sensor modalities. Our findings indicate that no singular solution surpasses all others, attributing to the inherent limitations of using a single sensor modality. As a result, we assert that integrating multiple sensor modalities presents a viable path toward devising a superior hand tracking solution. Ultimately, this survey paper aims to bolster interdisciplinary research efforts across the spectrum of hand tracking technologies, thereby contributing to the advancement of the field. [ABSTRACT FROM AUTHOR]

In many countries, agriculture plays a major role in the economy. The health of the crop is therefore very important, but there are many plant diseases that are difficult to diagnose. A close inspection is necessary in many cases, or an expert's advice is required. As a result, it is important to address diseases in plants. Several attempts have been made to develop programs that detect diseases in plants because of the ever-rising growth of computer vision and deep learning. This paper focuses on detecting diseases in guava fruits. We use a dataset with pictures of 4 common diseases found in the fruit namely Phytopthra, Red Rust, Scab, Styler and Root. With the help of transfer learning and Convolutional Neural Networks (CNN) this paper train various models on the dataset and compare the results. The results we evaluated using accuracy, precision, Recall and F1 score. We had multiple models with test accuracy of 99%, with highest accuracy of 99.62% for DenseNet169. The results of this study are also compared with previous methods. According to the results, the proposed methods achieved better results than the previous approach. [ABSTRACT FROM AUTHOR]

The application of object detection techniques in the field of unmanned aerial vehicles (UAVs) is an important research direction in computer vision. Because object detection in UAV aerial images needs to meet real-time requirements, a challenging problem in this technology is the trade-off between network parameters and detection accuracy. To solve this problem, this paper proposes a lightweight object detector family named YOLO-ERF. First, this paper proposes the effective receptive field (ERF) module, which can increase the convolutional kernel receptive field while preserving local details. The ERF module is then used to design a lightweight backbone to expand the network receptive field without the need for attaching additional context modules after the backbone to expand the receptive field. In addition, the proposed detectors use the ERF module to critically optimize the path aggregation network structure to improve accuracy with reduced network parameters. Finally, a lightweight detection head is proposed to improve small object recognition in complex backgrounds. With these optimizations, the YOLO-ERF models in this paper achieved a better trade-off between accuracy and parameters than other mainstream models, achieving strong results on the VisDrone and COCO datasets. YOLO-ERF-T reduced the number of network parameters by 40.3% when compared with YOLOv7-Tiny while increasing the average accuracy by 2.4% and 1.9%, respectively, in VisDrone and COCO datasets. [ABSTRACT FROM AUTHOR]

Accident detection and public traffic safety is a crucial aspect of safe and better community. Monitoring traffic flow in smart cities using different surveillance cameras plays a crucial role in recognizing accidents and alerting first responders. In computer vision tasks, utilizing action recognition (AR) has contributed to high-precision video surveillance, medical imaging, and digital signal processing applications. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for smart city. This paper focused on AR systems that use diverse sources of traffic video, such as static surveillance cameras on traffic intersections, highway monitoring cameras, drone cameras, and dash-cams. Through this review, we identified the primary techniques, taxonomies, and algorithms used in AR for autonomous transportation and accident detection. We also examined datasets utilized in the AR tasks, identifying the primary sources of datasets and features of the datasets. This paper provides a potential research direction to develop and integrate accident detection systems for autonomous cars and public traffic safety systems by alerting emergency personnel and law enforcement in the event of road traffic accidents to minimize the human error in accident reporting and provide a spontaneous response to victims. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

In this paper, we aim to implement augmented reality (AR) on distant text documents or books. For this purpose, we propose a new paper-based AR system that can detect text documents in real scenes, markerize and identify them, estimate their relative 3D poses to the camera, and augment them with virtual contents. Unlike the previous paper-based AR systems (applicable to only close documents), the proposed system not only requires no detection of words or characters, but allows partial occlusions like the previous systems. In our experiments, the proposed system worked at 24 fps and could consistently achieve high identification rates for both occluded and unoccluded pages. [ABSTRACT FROM AUTHOR]

In industry, robots are widely used to solve repetitive or dangerous actions in product production, so that product production can be more efficient. However, the problem that robots are often challenged is the convenience and the efficiency of introducing the production line. Therefore, the intuitive robot guidance method is an important issue; this paper will introduce the concept of human–robot interactions (HRI) and use deep learning methods on the machine vision system to complete the robot-guided assembly operation analysis, and the assembly operation analysis requires semantic segmentation as pre-processing. Therefore, we propose a novel semantic template correlation model architecture based on zero-shot learning (ZSL) to achieve the effect of rapid deployment. The semantic template correlation model is to search for the object area offline learning through the semantic template generated by the physics engine, and when inferring online, we can directly enter the semantic template to obtain the relevant object region. Finally, this paper verifies that the MIoU can be increased by more than 5% through the verification of the general database VOC2012. [ABSTRACT FROM AUTHOR]

Missing pixel imputation presents a critical challenge in image processing and computer vision, particularly in applications such as image restoration and inpainting. The primary objective of this paper is to accurately estimate and reconstruct missing pixel values to restore complete visual information. This paper introduces a novel model called the Enhanced Connected Pixel Identity GAN with Neutrosophic (ECP-IGANN), which is designed to address two fundamental issues inherent in existing GAN architectures for missing pixel generation: (1) mode collapse, which leads to a lack of diversity in generated pixels, and (2) the preservation of pixel integrity within the reconstructed images. ECP-IGANN incorporates two key innovations to improve missing pixel imputation. First, an identity block is integrated into the generation process to facilitate the retention of existing pixel values and ensure consistency. Second, the model calculates the values of the 8-connected neighbouring pixels around each missing pixel, thereby enhancing the coherence and integrity of the imputed pixels. The efficacy of ECP-IGANN was rigorously evaluated through extensive experimentation across five diverse datasets: BigGAN-ImageNet, the 2024 Medical Imaging Challenge Dataset, the Autonomous Vehicles Dataset, the 2024 Satellite Imagery Dataset, and the Fashion and Apparel Dataset 2024. These experiments assessed the model's performance in terms of diversity, pixel imputation accuracy, and mode collapse mitigation, with results demonstrating significant improvements in the Inception Score (IS) and Fréchet Inception Distance (FID). ECP-IGANN markedly enhanced image segmentation performance in the validation phase across all datasets. Key metrics, such as Dice Score, Accuracy, Precision, and Recall, were improved substantially for various segmentation models, including Spatial Attention U-Net, Dense U-Net, and Residual Attention U-Net. For example, in the 2024 Medical Imaging Challenge Dataset, the Residual Attention U-Net's Dice Score increased from 0.84 to 0.90, while accuracy improved from 0.88 to 0.93 following the application of ECP-IGANN. Similar performance enhancements were observed with the other datasets, highlighting the model's robust generalizability across diverse imaging domains. [ABSTRACT FROM AUTHOR]

The restoration of blurred images is a crucial topic in the field of machine vision, with far-reaching implications for enhancing information acquisition quality, improving algorithmic accuracy and enriching image texture. Efforts to mitigate the phenomenon of blur have progressed from statistical approaches to those utilizing deep learning techniques. In this paper, we propose a Generative Adversarial Network (GAN)-based image restoration method to address the limitations of existing techniques in restoring color and detail in motion-blurred images. To reduce the computational complexity of generative adversarial networks and the vanishing gradient during learning, an U-net-based generator is used, and it is configured to emphasize the channel and spatial characteristics of the original information through a proposed CSAR(Channel and Spatial Attention Residual) blocks module rather than a simple concatenate operation. To validate the efficacy of the algorithm, comprehensive comparative experiments have been conducted on the GoPro dataset. Experimental results show that the peak signal-to-noise ratio is improved compared to SRN and MPRNet algorithms with good image restoration ability. Objects detection experiments using Yolo V3 showed that the proposed algorithms can generate deblerring images with higher information quality. [ABSTRACT FROM AUTHOR]

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8’s high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems. [ABSTRACT FROM AUTHOR]

This paper explores the application of computer vision and mathematical modeling to analyze the intricate movements involved in weaving a traditional farming tool, the winnowing basket. By utilizing OpenPose algorithms, the study simplifies and visualizes the craftsmen's motions, particularly focusing on wrist movements. Video data of craftsmen in Chiba, Japan, creating Kizumi (place name) winnowing baskets is used as the basis for analysis. The extracted information is used to generate 2D motion trajectories of the wrist, allowing a comparison between beginners who watched parsed videos and those who watched the original videos in terms of skill acquisition and learning time. By visualizing human body behavior and combining statistical results, this study demonstrates the potential of artificial intelligence techniques such as computer vision for observing repetitive human movement and inheriting traditional skills. [ABSTRACT FROM AUTHOR]

Scene classification and recognition have always been one of the most challenging tasks of scene understanding due to the inherent ambiguity in visual scenes. The core of scene classification and recognition tasks is scene representation. Deep learning advances in computer vision, especially deep CNNs, have significantly improved scene representation in the last decade. Deep convolutional features extracted from deep CNNs provide discriminative representations of the images and are widely used in various computer vision tasks, such as scene classification. Deep convolutional features capture the appearance characteristics of the image and the spatial information about different image regions. Meanwhile, the semantic and context information obtained from high-level concepts about scene images, such as objects and their relationships, can significantly contribute to identifying scene images. Therefore, in this paper, we divide visual scenes into two categories, object-based and layout-based. Object-based scenes are scenes that have scene-specific objects and, based on those objects, can be described and identified. In contrast, the layout-based scenes do not have scene-specific objects and are described and identified based on the appearance and layout of the image. This paper proposes a new neural network model for representing and classifying visual scenes, which we call G-CNN (GNN-CNN). The proposed model includes two modules, feature extraction and feature fusion, and the feature extraction module composes of visual and semantic branches. The visual branch is responsible for extracting deep CNN features from the image, and the semantic branch is responsible for extracting semantic GNN features from the scene graph corresponding to the image. The feature fusion module is a novel two-stream neural network that fuses the CNN and GNN feature vectors to produce a comprehensive representation of the scene image. Finally, a fully-connected classifier classified the obtained comprehensive feature vector into one of the pre-defined categories. The proposed model has been evaluated on three benchmark scene datasets, UIUC Sports, MIT67, and SUN397, and obtained classification accuracy of 99.91%, 96.01%, and 85.32%, respectively. In addition, a new dataset named Scene40, which has been introduced in our previous paper, is also used for further evaluation of the proposed method. The comparison results based on classification accuracy criteria show that the proposed model can outperform the best previous methods on three benchmark scene datasets. [ABSTRACT FROM AUTHOR]

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India's linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. [ABSTRACT FROM AUTHOR]

Waste generation has seen a significant surge in the last decade, presenting an urgent need for efficient and sustainable waste management strategies. The mounting piles of landfill waste underscore the criticality of segregating recyclable from non-recyclable waste, a measure that could alleviate numerous global environmental challenges. The increasing necessity to differentiate waste into specific categories such as paper, plastic, and metal is becoming evident; these categories demand distinct disposal and recycling methods. Addressing this automatic detection and segregation issue calls for an automated garbage classification system, achievable through computer vision. This paper introduces a deep learning and computer vision-based Smart Ensembled Framework for Waste Management (SEFWaM) to categorize garbage into various classes for improved waste management. This innovative model employs a fusion of transfer learning—a deep learning technique and boosting to devise an ensembled approach for efficient waste classification. Our proposed approach, trained on the Trashnet 2.0 dataset, has demonstrated superior performance over competing algorithms in terms of Weighted Macro Precision (WMP), Weighted Macro Recall (WMR), Weighted Macro F1-Score (WMF), and test accuracy. The proposed SEFWaM model achieves an accuracy of 94.2% for the considered dataset, proving its superior efficiency over other deep learning-based models. This research thereby contributes to a pressing environmental issue by offering an automated, efficient solution for waste management. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information about the building stock is fundamental to better understand and mitigate the impact caused by catastrophic earthquakes, as seen recently in Turkey, Syria, Morocco and Afghanistan. Planning for such events is necessary to increase the resilience of the building stock and to minimize casualties and economic losses. Although in several parts of the world new constructions follow more strict compliance with modern seismic codes, a large proportion of existing building stock still demands a more detailed and automated vulnerability analysis. Hence, this paper proposes the use of computer vision deep learning models to automatically classify buildings and create large scale (city or region) exposure models. Such approach promotes the use of open databases covering most cities in the world (cf. OpenStreetMap, Google Street View, Bing Maps and satellite imagery), Therefore providing valuable geographical, topological and image data that may cheaply be used to extract valuable information to feed exposure models. Our previous work using deep learning models achieved, in line with the results from other projects, high classification accuracy concerning building materials and number of storeys. This paper extends the approach by: (i) implementing four CNN-based models to perform classification of three sets of different/extended buildings’ characteristics; (ii) training and comparing the performance of the four models for each of the sets; (iii) comparing the risk assessment results based on data extracted from the best CNN-based model against the results obtained with traditional ground data. In brief, the best accuracy obtained with the three tested sets of buildings’ characteristics is higher than 80%. Moreover, it is shown that the error resulting from using exposure models fed by automatic classification is not only acceptable, but also far outweighs the time and costs of obtaining a manual and specialised classification of building stocks. Finally, we recognize that automatic assessment of certain complex buildings’ characteristics compares to similar limitations of traditional assessments performed by specialized civil engineers, typically related with the identification of the number of storeys and the construction material. However, the identified limitations do not show worse results when compared against the use of manual buildings’ assessment.Article Highlights: Implement an AI/ML framework for automating the collection of buildings’ façades pictures annotated with several characteristics required by Exposure Models. Collect, process and filter a 4.239 pictures dataset of buildings’ façades, which was made publicly available. Train, validate and test several Deep Learning models using 3 sets of building characteristics to produce exposure models with accuracies above 80%. Use heatmaps to show which image areas are more activated for a given prediction, thus helping to explain classification results. Compare simulation results using the predicted exposure model and a manually created exposure model, for the same set of buildings. [ABSTRACT FROM AUTHOR]

Image restoration is a fundamental problem in computer vision, with the goal of restoring high-quality images from degraded low-quality observation images. However, the ill-posedness of restoration problem often leads to artifacts in the results. It inspires us to study the combination of prior, effectively restrict the solution space and improving the quality of the restored image. In this paper, a novel hybrid regularization method for image restoration is proposed, which adopts both internal and external image priors into a unified framework. Specifically, a new hybrid regularization model is designed. The TV model and a deep image denoiser are inserted into the restoration model using the plug-and-play framework, protecting image details while implicitly using external information for deep denoising. Moreover, in order to make the proposed hybrid regularization operable, an adaptive parameter method is proposed to adaptively balance the TV model and learned model in iteration. Experiments show that the proposed method surpasses the performance of existing image restoration techniques. [ABSTRACT FROM AUTHOR]

The use of object detection technology in unmanned aerial vehicles is a crucial area of research in computer vision. Aerial images captured by drones exhibit differences in object shape and size compared to traditional images, which can cause object detection algorithms to miss or misidentify small targets. This paper makes improvements based on the YOLOv5 algorithm. The algorithm introduces a small target detection layer to improve the model's detection capability at different scales. Cross-channel fusion module and multi-level feature fusion downsampling module are added to obtain more comprehensive context information. This makes the network pay more attention to the important features of small targets. Additionally, the classification task and regression task of the detection head are decoupled to speed up the model's convergence and improve detection accuracy. Finally, a new loss function is proposed to further improve the accuracy and convergence rate of the detector. The algorithm is evaluated on the VisDrone2019 dataset and compared with the YOLOv5s algorithm. The results show an improvement of 4.7% in mAP0.5, 3.0% in mAP0.5:0.95, 3.6% in precision, and 6.4% in recall. At the same time, the algorithm was evaluated on the DIOR dataset, and mAP0.5:0.95 improved by 1.5%.These findings demonstrate the algorithm's effectiveness in detecting small targets in aerial images captured by drones. [ABSTRACT FROM AUTHOR]

Monitoring and evaluating fish growth and health relies on accurate aquaculture body size and weight data. Pearl's gentian grouper has tender flesh and rapid growth, and the scale of aquaculture is increasing yearly. Moreover, it is inefficient to measure body size and weight data manually regularly, so in this paper, taking pearl gentian grouper as the research object, we propose a body size and weight estimation method based on machine vision and improve DeepLabV3 + algorithm, with the MIoU reaching 97.37% on the self-constructed dataset, to extract the closed contour of the fish body curve, then apply the algorithm for measuring body scale traits. This paper determines the critical points of body scale calculation. It uses the calibration coefficients to calculate the body scale parameters and then estimate fish body weight. In the established body weight estimation model, the coefficient of determination of the CatBoost regression model based on the total length, body height, body thickness, and area of the fish was R2 of 0.987 which realized the high-precision estimation of the body weight of the pearl gentian grouper and provided the basis for intelligent grading. [ABSTRACT FROM AUTHOR]

With growing demands for higher image quality in the fields of film, video post-production, image restoration, art creation, and computer vision, color transfer between images has become an important research area. Based on previous research on color transfer techniques, this paper proposes a color transfer method for images based on saliency features, aiming at automatic color migration between them. Transferring colors based on the saliency features of the input image can avoid the problem of unnatural color of the output image due to mixing of colors from different regions. First, the local variances of both the original and reference images are calculated, serving as a temporary saliency feature map. This is followed by obtaining a refined saliency feature map after undergoing processes such as minimization filtering, binarization, expansion, and iteration. Subsequently, color is transferred between the saliency and non-saliency regions of the original and reference images. To avoid the generation of pseudo-contours, the image is then refined using base projection. Finally, an output image is obtained by fusing the base-projected image with the outcome from Reinhard's method, ensuring the output retains its naturalness and consistency. We conducted experiments with different types of images such as natural landscapes, buildings, and art paintings. The experimental results show that the method proposed in this paper not only retains the intricacies of the original image but also offers fuller and more realistic color renditions. [ABSTRACT FROM AUTHOR]

Classification algorithm is the crucial technique for machine vision system. The Transfer Component Analysis has been introduced for workpiece classification system. The dataset with five classes of workpieces is constructed for experiments, and the data augmentation strategies have been employed to prevent class imbalance problems. The Histogram of the Oriented Gradient features are extracted over workpiece images, and they are transformed by the Transfer Component Analysis and the Principal Component Analysis separately. The Maximum Mean Discrepancy distances have been employed to measure the distribution distances between the training set and the testing set. And the results indicate that the Transfer Component Analysis has dramatically reduced the distribution differences in a dimensionality reduction subspace, better than the Principal Component Analysis. The Error Correction Output Code is combined with Support Vector Machine and K-Nearest Neighbor respectively as the classifiers. The macro-averaged F 1 measure is adopted to evaluate the classifiers performance. Experiment results show that, the Transfer Component Analysis is particularly beneficial for improving the classification performance, and the Support Vector Machine is more favorable than the K-Nearest Neighbor for workpiece classification. The method of the Transfer Component Analysis combined with the Support Vector Machine possesses the best classification performance among these methods in this paper. [ABSTRACT FROM AUTHOR]

In identifying crop leaf diseases, Due to the complex nature of the disease symptoms. There may be variations in disease symptoms with similar characteristics and similarities in disease symptoms with different elements. This can make it challenging to differentiate between various diseases. CSASNet is a hybrid classification model proposed in this paper that combines the attention and multiscale feature fusion mechanisms. The model first incorporates the multiscale feature fusion module atrous spatial pyramid pooling (ASPP) into the ShuffleNetV2 network structure. This enriches the network with disease-specific multiscale feature information. Additionally, the model combines the special group-wise enhance (SGE) attention mechanism module to enhance the weight of disease spot feature information. Lastly, the leaky ReLU function replaces the original ReLU activation function. This allows the model to reduce damaging feature loss during training. The paper presents a design of multiple cross-validation experiments for comparison. The experimental results suggest that the improved model was used for disease leaf identification and showed an accuracy improvement on different crops. Compared to Convnext and MobileNetV2, the CSASNet model demonstrates higher recognition accuracy. [ABSTRACT FROM AUTHOR]

This Special Issue presents extended versions of the best papers presented at the International Conference on Digital Image Computing: Techniques and Applications (DICTA) which is the flagship Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA is technically cosponsored by the Institute of Electrical and Electronics Engineers (IEEE) and the Australian Pattern Recognition Society (APRS). [Extracted from the article]

This special issue of the International Journal of Computer Vision (IJCV) presents the best papers of the 28th British Machine Vision Conference (BMVC) held in Imperial College London, UK, on 4th-7th September 2017. The papers presented in this issue offer a snapshot of some of the best work in the field, on the topics of (1) learning quantised representations by deep neural networks, (2) 3D shape estimation, (3) novel image representations, (4) image descriptors and matching, and (5) data generation. I Image descriptors and matching i by local descriptors or deep neural networks remains an active research topic. [Extracted from the article]

The detection and tracking of the small target in infrared video are among the most critical technologies in computer vision applications. These include video surveillance and infrared imaging precision guidance. Recently, more and more infrared small target detection and tracking algorithms have been proposed. However, most existing algorithms have complex processing problems, high false alarm rates, and low detection accuracy. To achieve accurate detection and tracking of infrared small targets, this paper proposes an algorithm for detection and tracking of infrared small targets using infrared small target feature point and gradient information. Feature point detection is used to detect possible targets, and possible targets are further processed through direction gradient calculation. Then, in the adjacent sequence images, it is matched according to the local features of adjacent frames. According to the characteristics of infrared small target motion, this paper proposes a target motion generation trajectory to verify the accuracy of the detection algorithm. Finally, compared with other algorithms, it is concluded that the algorithm in this paper has a higher detection rate and a lower false detection rate. [ABSTRACT FROM AUTHOR]

With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of 3 × 3 is improved to a convolution kernel of 2 × 2 in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate. [ABSTRACT FROM AUTHOR]

Image - Caption Generator is a popular Artificial Intelligence research tool that works with image comprehension and language definition. Creating well-structured sentences requires a thorough understanding of language in a systematic and semantic way. Being able to describe the substance of an image using well-structured phrases is a difficult undertaking, but it can have a significant impact in terms of assisting visually impaired people in better understanding the images' content. Image captions has gained a lot of attention as a study subject for various computer vision and natural language processing (NLP) applications. The goal of image captions is to create logical and accurate natural language phrases that describes an image. It relies on the caption model to see items and appropriately characterise their relationships. Intuitively, it is also difficult for a machine to see a typical image in the same way that humans do. It does, however, provide the foundation for intelligent exploration in deep learning. In this review paper, we will focus on the latest in-depth advanced captions techniques for image captioning. This paper highlights related methodologies and focuses on aspects that are crucial in computer recognition, as well as on the numerous strategies and procedures being developed for the development of image captions. It was also observed that Recurrent neural networks (RNNs) are used in the bulk of research works (45%), followed by attention-based models (30%), transformer-based models (15%) and other methods (10%). An overview of the approaches utilised in image captioning research is discussed in this paper. Furthermore, the benefits and drawbacks of these methodologies are explored, as well as the most regularly used data sets and evaluation processes in this sector are being studied. [ABSTRACT FROM AUTHOR]

Industrial defect detection is a hot topic in the field of computer vision. It is a challenging task due to complex features and many categories of industrial defects. In this paper, a deep learning model based on the multiscale feature extraction module is introduced for steel surface defect detection. The main focus on the feature extraction capability of the model and feature fusion capability to improve the accuracy of the model for steel surface defect detection. First, to improve the feature extraction ability of the model, a multiscale feature extraction (MSFE) module is introduced. The MSFE module can effectively extract multiscale features through three branches that have different convolution kernel sizes. Second, an efficient feature fusion (EFF) module is proposed to optimize feature fusion by adding features from the backbone network to the neck network. Third, this paper puts forward a new Bottleneck module by reducing the normalization layer and activation function in the original Bottleneck module. Finally, the backbone network is deepened to further enhance the feature extraction ability of the model. Extensive experiments are conducted on the public NEU-DET dataset. The experimental results validate the effectiveness of the designed modules and the proposed model. Compared with other state-of-the-art methods, the proposed model achieves optimal accuracy(73.08% mAP@0.5) while maintaining a small number of parameters. [ABSTRACT FROM AUTHOR]

Manufacturing industry often uses 3D scanning technologies to inspect their products. Some of these techniques produce a point cloud that represent a section of the manufactured product. The clouds must be aligned to the model of the product in order to check its quality. Current registration methods are usually affected by dimensional problems or volumetric anomalies. This paper proposes a new method for the registration process aligning the cloud to the model in several steps. The first step is the state-of-the-art method. The second step uses the information acquired in the first one to perform a fine registration in order to not being affected by dimensional defects or little miss alignments in the previous step providing a huge improvement in the measurement of surface defects. In this paper, several techniques are proposed in order to provide a set of tools that the final users can tune to fit their needs. The whole procedure of registration can be run in real-time conditions using the sampling and caching strategies proposed. The methods proposed are tested over more than 10,000 profiles of a rail proving they successfully align the cloud and the model providing better results in the measurement of surface defects. [ABSTRACT FROM AUTHOR]

Low-value recyclable waste (LVRW) is an essential component of municipal domestic waste. Due to the lightweight and high quantity characteristics, the main disposal methods of LVRW are incineration and landfill, which are not conducive to the environmental protection requirements and the goal of carbon neutrality and emission peak. The paper focuses on the delicate separation of LVRW to realize a cost-effective solution for waste sorting. Firstly, a machine vision (MV) pneumatic sorting equipment was designed based on the MV detection system. Then, a large number of authentic waste images were captured and labeled to train a high accuracy LVRW prediction model through semi-automatic labeling and transfer learning. Subsequently, the LVRW waste sorting solution was designed and constructed, and a comparative experimental study about the MV sorting method and the whole solution was conducted. The experimental results show that the MV sorting method achieves 92% sorting accuracy, and the solution can dispose of 65 tons of LVRW per day with a waste recycling rate of 37.7%. The sorting equipment and solutions developed in the paper can effectively improve the resource utilization of LVRW at a lower cost. [ABSTRACT FROM AUTHOR]

The deep fusion of sports and machine vision has become a research hot spot in sports video target detection, athlete state recovery and sports promotion. On the basis of in-depth study, it can detect a large number of sports videos, complete the drawing and analysis of human body detection model, and detect and evaluate the posture of corresponding athletes in the video, which can save a lot of costs and maximize the more professional training of athletes. In order to solve the above problems, this paper innovatively completes the automatic language description of sports video based on time-sharing memory algorithm. Its principle is to realize the accurate decomposition of athletes' sports data through the mapping relationship between the corresponding letter sequence and video sequence in time-sharing memory. In order to capture the key posture of athletes' sports video, this paper innovatively proposes an object extraction algorithm based on athletes' skeleton motion enhancement. In practical application, based on the key pose capture, it is necessary to train the depth selection network in time to extract the key pose of the skeleton. Based on this network, it can enhance the key posture of bone information and accurately express its related features. After extracting the actual athlete's bone information, we need to fine-tune the training network to realize the accurate recognition of key features. Based on the above key algorithms, this paper designs a sports video athlete detection system based on deep learning and makes an experimental research on the related sports video. The experimental results show that the detection accuracy of athletes' sports video is improved by nearly 10% compared with the traditional convolution network recognition algorithm, so the algorithm has obvious advantages in recognition accuracy. [ABSTRACT FROM AUTHOR]

These days, the integration of big data and information systems enables industries such as sports, health, and medical sciences in resource optimization by enhancing their operational efficiency. Personalized health management, driven by advancements in big data analysis, feature extraction, and internet of things (IoT) has emerged as a prominent trend in the sports industry. Based on these advancements, this paper focuses on leveraging machine vision, big data, and IoT to conduct a comprehensive study for extracting football players' offense features by aiming to improve accuracy and ensure the players' safety in sports industry. First, this paper discusses a framework for sports health, which is based on big data analysis. Next, we discuss the use of big data and feature extraction for IoT and sports health informatics. The action image of an offense for football players is collected by an intelligent camera, and the gathered imaging code is read and analyzed by radio frequency technology. Using the recognition method of offense action using deep learning and motion information, the offense action's image features and the three-color features of video RGB are fused into deep learning features to complete the offense action recognition. Based on the offense action recognition, the Harris 3D operator is applied to construct the sequence potential function of the offense action, and the sequence possible function of the offense action is used as the operational technique for this purpose. In addition, the AdaBoost algorithm is used to comprehensively screen the characterized data of athletes' offense actions, and the filtered data are used as training samples to complete the feature extraction of athletes' offense actions. The experimental results show that the proposed method can effectively reduce the recognition error of offense action and improve the efficiency of action's feature extraction and the accuracy of feature extraction. [ABSTRACT FROM AUTHOR]