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Abstract 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 Background In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

One of the most popular and well-established forms of payment in use today is paper money. Handling paper money might be challenging for those with vision impairments. Assistive technology has been reinventing itself throughout time to better serve the elderly and disabled people. To detect paper currency and extract other useful information from them, image processing techniques and other advanced technologies, such as Artificial Intelligence, Deep Learning, etc., can be used. In this paper, we present a meticulously curated and comprehensive dataset named ‘NSTU-BDTAKA’ tailored for the simultaneous detection and recognition of a specific object of cultural significance - the Bangladeshi paper currency (in Bengali it is called ‘Taka’). This research aims to facilitate the development and evaluation of models for both taka detection and recognition tasks, offering a rich resource for researchers and practitioners alike. The dataset is divided into two distinct components: (i) taka detection, and (ii) taka recognition. The taka detection subset comprises 3,111 high-resolution images, each meticulously annotated with rectangular bounding boxes that encompass instances of the taka. These annotations serve as ground truth for training and validating object detection models, and we adopt the state-of-the-art YOLOv5 architecture for this purpose. In the taka recognition subset, the dataset has been extended to include a vast collection of 28,875 images, each showcasing various instances of the taka captured in diverse contexts and environments. The recognition dataset is designed to address the nuanced task of taka recognition providing researchers with a comprehensive set of images to train, validate, and test recognition models. This subset encompasses challenges such as variations in lighting, scale, orientation, and occlusion, further enhancing the robustness of developed recognition algorithms. The dataset NSTU-BDTAKA not only serves as a benchmark for taka detection and recognition but also fosters advancements in object detection and recognition methods that can be extrapolated to other cultural artifacts and objects. We envision that the dataset will catalyze research efforts in the field of computer vision, enabling the development of more accurate, robust, and efficient models for both detection and recognition tasks.

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]

Abstract Due to migration, terror‐threats and the viral pandemic, various EU member states have re‐established internal border control or even closed their borders. European Association for Biometrics (EAB), a non‐profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re‐establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re‐establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade‐off with regards to open borders while maintaining a high‐level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions.

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]

Along with the improvement of quality requirements industrial production, surface inspection of workpiece has gradually become an indispensable and important process in the production of the workpiece. Aiming at the traditional methods in textured paper inspection, there are problems of low efficiency and large error based on machine vision, we propose a "photometric stereo vision + fast Fourier enhancement + feature fusion" composite structure inspection method. First, as the traditional CCD camera produces obvious noise and scratches, which are difficult to distinguish from the background texture area, we propose combining the photometric stereo vision measurement algorithm to get the surface gradient information of the textured paper to obtain more gradient texture information; and then realize the secondary enhancement of the image through Fourier transform in spatial and frequency domains. Second, as the textured paper scratches are difficult to detect, the features are difficult to extract, and the threshold boundary is difficult to define, we propose dynamic threshold segmentation through multi-feature fusion to realize the surface scratch detection work of textured paper. We designed experiments using more than 300 different textured papers; and the results show that the composite structure detection method proposed in this paper is feasible and has advantages. [ABSTRACT FROM AUTHOR]

Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Various techniques were combined to optimize an optical inspection system designed to automatically inspect defects in manufactured paper bowls. A self-assembled system was utilized to capture images of defects on the bowls. The system employed an image sensor with a multi-pixel array that combined a complementary metal-oxide semiconductor and a photo detector. A combined ring light served as the light source, while an infrared (IR) LED matrix panel was used to provide constant IR light to highlight the outer edges of the objects being inspected. The techniques employed in this study to enhance defect inspections on produced paper bowls included Gaussian filtering, Sobel operators, binarization, and connected components. Captured images were processed using these technologies. Once the non-contact inspection system's machine vision method was completed, defects on the produced paper bowls were inspected using the system developed in this study. Three inspection methods were used in this study: internal inspection, external inspection, and bottom inspection. All three methods were able to inspect surface features of produced paper bowls, including dirt, burrs, holes, and uneven thickness. The results of our study showed that the average time required for machine vision inspections of each paper bowl was significantly less than the time required for manual inspection. Therefore, the investigated machine vision system is an efficient method for inspecting defects in fabricated paper bowls. [ABSTRACT FROM AUTHOR]

The article discusses the use of computer vision in analyzing the material nature of art. Topics mentioned include the importance of understanding how artworks are made and the inspirations of artists, the examination of surface textures of photographic prints, and the photomechanical reproduction of images.

In this paper, the paper cups were used as the research objects, and the machine vision detection technology was combined with different image processing techniques to investigate a non-contact optical automatic detection system to identify the defects of the manufactured paper cups. The combined ring light was used as the light source, an infrared (IR) LED matrix panel was used to provide the IR light to constantly highlight the outer edges of the detected objects, and a multi-grid pixel array was used as the image sensor. The image processing techniques, including the Gaussian filter, Sobel operator, Binarization process, and connected component, were used to enhance the inspection and recognition of the defects existing in the produced paper cups. There were three different detection processes for paper cups, which were divided into internal, external, and bottom image acquisition processes. The present study demonstrated that all the detection processes could clearly detect the surface defect features of the manufactured paper cups, such as dirt, burrs, holes, and uneven thickness. Our study also revealed that the average time for the investigated Automatic Optical Detection to detect the defects on the paper cups was only 0.3 s. [ABSTRACT FROM AUTHOR]

Due to migration, terror‐threats and the viral pandemic, various EU member states have re‐established internal border control or even closed their borders. European Association for Biometrics (EAB), a non‐profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re‐establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re‐establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade‐off with regards to open borders while maintaining a high‐level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions. [ABSTRACT FROM AUTHOR]

During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks. [ABSTRACT FROM AUTHOR]

Purpose -- Conference publications are an important aspect of research activities. There are generally both oral presentations and poster sessions at large international conferences. One can hypothesise that, for the same conferences, the papers presented in oral sessions should have a higher research impact than the papers presented in poster sessions. However, there has been no related study examining the validity of this hypothesis. In other words, the difference of research impact between papers presented orally or during poster sessions has not been discussed in literature. Therefore, the purpose of this paper is to conduct a citation analysis to compare the research impact of papers presented in oral and poster sessions. Design/methodology/approach -- In this paper, data from three leading conferences in the field of computer vision are examined, namely CVPR (2011 and 2012), ICCV (2011) and ECCV (2012). Several types of citation-related statistics are collected, including the number of highly cited papers (i.e. high number of citations) presented in oral and poster sessions, the total citations of both types of papers, the average citations of oral and poster papers, and the average citations of each frequently cited paper of both types. Findings -- There are three main findings. First, a larger proportion of highly cited papers are from oral sessions than poster sessions. Second, the average number of citations per paper is larger for those presented in oral sessions than poster sessions. Third, the average number of citations for highly cited papers presented in oral sessions is not necessarily greater than for the ones presented in poster sessions. Originality/value -- The originality of this paper is that it is the first attempt to examine the differences of citation impacts of conference papers presented in oral and poster sessions. The findings of this study will allow future bibliometrics research to further explore this related issue for longer periods and different fields. [ABSTRACT FROM AUTHOR]

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area. [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]

We develop a vision system for batch inspection by oven white paper model color by manufacturing a machine vision system for the oven manufacturing automation process. In the vision system, white paper object detection (spring), color clustering, and histogram extraction are performed. In addition, for the automated process of home appliances, we intend to develop an automatic mold combination detection algorithm that inspects the label position and direction (angle/coordinate) using deep learning. [ABSTRACT FROM AUTHOR]

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf‐level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data‐driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists.

Measurement related to different machine vision functions is the base for developing of cyber-physical systems able to see and make decisions. These kinds of systems are emerging in all areas of our daily lives. They can be found in the medical area, in industry, in the agriculture, in all those interconnected cloud computing-based systems related to flying/terrestrial robotics, navigation, automated surgery, smart cities, smart health monitoring, etc. All of them are extremely dependent on the same: adequate coordinates measurement, properly selected data processing and data fusion algorithms, evaluation procedures for performance analysis of measurement within Machine Vision systems, processes and algorithms (both traditional and artificial intelligence), mathematical models for 3D-measurement purposes (measurement of displacements, surface profiles, deformations, data augmentation/interpolation, etc.), and distributed visual measurement systems, as well as distributed memory and sensory part. Cyber-physical systems can be implemented on almost any application, especially on those dotted by robots and automated guided devices (from aerospace applications to domestic cleaners). The success of the measurement process depends on the kind of sensors and their optoelectronics characteristics and intrinsic parameters, as well as their respective operating and processing. The correct approach selection for the application, the data acquisition and collection efficiency, the data processing algorithms, the hardware processors response time, and the intelligent auto adaptability to changing environments or conditions. Recently, the emergence of artificial intelligence algorithms and the internet of things have powerful development of such systems, highlighting the importance and the impact of the measurement accuracy related to machine vision performance. [ABSTRACT FROM AUTHOR]

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Baggage mishandling has received much attention by airport operators. Traditional baggage tracking methods (e.g., manual, barcode, and radio-frequency identification) have not been able to deal with the challenge of baggage mishandling due to their unreliable and inefficient performance. Baggage data comprises sensitive personal information, linking individuals to their personal details and travel history, with the potential to expose security vulnerabilities. This paper proposes a smart baggage tracking system based on surveillance and blockchain technology for mitigation of baggage mishandling at airports. Surveillance including a network of airport cameras is used to recognize and monitor locations of baggage and passengers. Blockchain technology is utilized to manage and process baggage and passenger databases, guaranteeing the security, privacy, and transparency of baggage information. Surveillance-captured images of baggage and passengers undergo processing through computer vision algorithms to determine the current whereabouts of baggage, subsequently synchronized and updated within the blockchain storage. Additionally, a user interface is developed to present real-time baggage tracking information. Preliminary experiments have demonstrated the applicability of the smart baggage tracking system for airports. [ABSTRACT FROM AUTHOR]

Abstract: This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques. [Copyright &y& Elsevier]

We propose an efficient method to model paper tearing in the context of interactive modeling. The method uses geometrical information to automatically detect potential starting points of tears. We further introduce a new hybrid geometrical and physical-based method to compute the trajectory of tears while procedurally synthesizing high resolution details of the tearing path using a texture based approach. The results obtained are compared with real paper and with previous studies on the expected geometric paths of paper that tears. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score. [ABSTRACT FROM AUTHOR]

As the fundamental problem in the computer vision area, image matching has wide applications in pose estimation, 3D reconstruction, image retrieval, etc. Suffering from the influence of external factors, the process of image matching using classical local detectors, e.g., scale-invariant feature transform (SIFT), and the outlier filtering approaches, e.g., Random sample consensus (RANSAC), show high computation speed and pool robustness under changing illumination and viewpoints conditions, while image matching approaches with deep learning strategy (such as HardNet, OANet) display reliable achievements in large-scale datasets with challenging scenes. However, the past learning-based approaches are limited to the distinction and quality of the dataset and the training strategy in the image-matching approaches. As an extension of the previous conference paper, this paper proposes an accurate and robust image matching approach using fewer training data in an end-to-end manner, which could be used to estimate the pose error This research first proposes a novel dataset cleaning and construction strategy to eliminate the noise and improve the training efficiency; Secondly, a novel loss named quadratic hinge triplet loss (QHT) is proposed to gather more effective and stable feature matching; Thirdly, in the outlier filtering process, the stricter OANet and bundle adjustment are applied for judging samples by adding the epipolar distance constraint and triangulation constraint to generate more outstanding matches; Finally, to recall the matching pairs, dynamic guided matching is used and then submit the inliers after the PyRANSAC process. Multiple evaluation metrics are used and reported in the 1st place in the Track1 of CVPR Image-Matching Challenge Workshop. The results show that the proposed method has advanced performance in large-scale and challenging Phototourism benchmark. [ABSTRACT FROM AUTHOR]

For those who are deaf or hard of hearing, sign language is essential as their main form of communication. using ease, sign language gestures may be translated into written or spoken words in real time using the Sign Language Translator, and vice versa. This system interprets and communicates sign language gestures by utilising computer vision and natural language processing (NLP). Given that sign language uses a wide range of hand movements to communicate meaning, it might be difficult to identify certain motions by looking for patterns. Individuals communicate and engage using a variety of gestures. In this study, a human-computer interface that can recognise motions in sign language and properly translate them into text is shown. The suggested method improves interpersonal communication by using convolutional neural networks and long short-term memory networks for gesture interpretation and detection. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

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To automatic detect and characterize paper impurities with computer vision, we present a novel two parts evaluation procedure with feature representations using Alternating Direction Method of Multipliers (ADMM) sparse codes. The method is based on an offline training step to obtain sparse coefficients and codebooks via learning extracted features with ADMM optimization, followed by an online detection step to use linear SVM classifier to assess defective paper samples from no-defective ones. Our approach bridges the gap between paper impurities evaluation and sparse feature representations, taking advantages of existing ADMM algorithms to handle sparse codes problem. We compare different feature descriptors and sparse code methods to implement the procedure and experimentally validate it on a dataset of 11 paper classes. Experiment results show that the proposed method is competitive and effective in terms of evaluation accuracy and speed. [ABSTRACT FROM AUTHOR]

Abstract: Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the paper''s geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings. [Copyright &y& Elsevier]

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers. [ABSTRACT FROM PUBLISHER]

A better understanding of paper properties requires a detailed knowledge about the spatial arrangement of its constituent materials in its structure. This paper presents a novel approach for the analysis of the three-dimensional paper structure at the fibre level. A technique combining a rotary microtome and an optical microscopy was developed allowing serial sectioning of hundreds of cuts. The microscope is fixed on a moveable stage and mounted in front of a microtome. Repeatedly, thin slices are cut off an embedded paper sample and the cut block surface is scanned in a fully automated process. The prototype built is able to digitize paper samples with a size of more than 1 cm at a possible three-dimensional resolution below 1 μm. Advanced computer vision methods are applied to extract relevant information from the digitized samples. Currently, the most important applications are the analysis of pigment coating layers on the paper surfaces and the analysis of fibre transverse morphology. Besides the analysis of paper structures, this technique is also suited for the spatial analysis of other materials, if the structural features are accessible with light optical microscopy. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a mobile application, built using Flutter, that leverages object detection to enhance the lives of visually impaired individuals. The application addresses a crucial challenge faced by this community: the lack of real-time information about their surroundings. A solution is proposed that utilizes pre-trained machine learning models, potentially through TensorFlow Lite for on-device processing, to identify objects within the user's field of view as captured by the smartphone camera. The application goes beyond simple object recognition; detected objects are translated into natural language descriptions through text-to-speech functionality, providing crucial auditory cues about the environment. This real-time information stream empowers users to navigate their surroundings with greater confidence and independence. Accessibility is a core principle of this paper. The user interface will be designed for compatibility with screen readers, ensuring seamless interaction for users who rely on assistive technologies. Haptic feedback mechanisms will be incorporated to provide non-visual cues and enhance the user experience. The ultimate goal of this paper is to create a user-friendly and informative application that empowers visually impaired individuals to gain greater independence in their daily lives. The application has the potential to improve spatial awareness, foster a sense of security, and promote overall inclusion within society. [ABSTRACT FROM AUTHOR]

How to improve the high resolution and remove the noise of composite infrared thermal wave detection image have become important research topics in machine vision. Therefore, this paper proposes a composite image enhancement algorithm based on edge adaptive total variation of direction. Using the improved adaptive directional total variation model to construct the guidance image of the guidance filter can provide better structural information for the guidance filter and conduct the guidance filter. This process is iterated to eliminate noise and ladder effect. Experimental results show that this algorithm is superior to other advanced methods in objective evaluation index and subjective vision. The algorithm proposed in this paper can not only effectively eliminate noise and ladder effect, but also highlight image details and structure information, which proves the effectiveness of this algorithm. Discover the cutting-edge algorithm for high-resolution improvement and noise elimination in infrared thermal wave detection images. Our innovative composite image enhancement technique, based on edge adaptive total variation, outperforms traditional methods. The improved directional model provides better structural information, optimizing the guidance filter process. Experimental results reveal its superiority in objective evaluation and subjective vision. This algorithm effectively eliminates noise and ladder effect while highlighting image details, validating its remarkable effectiveness. [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]