External Image Research Articles

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Abstract Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions.
Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.
Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network (CNN) denoiser.
To make the proposed reconstruction problem easier to tackle, we utilize the Alternate Direction Method of Multipliers (ADMM) for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of 1×10^5 and 1×10^6, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.
Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

ESPressoscope: A small and powerful approach for in situ microscopy.

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. Our prototype of this approach, which we call ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. Our prototype of the ESPressoscope concept achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage our prototype's software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications.

‘Into the darkness’ – The caving community and their tourist experiences. The Polish case

Descent into the underground requires from explorers not only a high level of physical endurance, technical skills, and ability to use specialized equipment, but also psychological adaptation to severe cave conditions: the darkness, the coldness, and the unknown. The paper focuses on mental states and emotions that accompany cave descents at every stage of the tourist experience. The study examines cavers' tourist motivations as well as perceived benefits of this kind of physical activity. The research aims at the Polish caving community. The study indicates that participation in cave exploration for respondents undertaking cave challenges outside of regular tourism is generally the result of a combination of equally strong factors, rather than a single dominant motivation. The benefits of cave exploration are inwardly directed, those related to creating one’s external image were of marginal importance among survey respondents. A state of ‘focus and concentration’ accompanies cavers during almost the entire stay in cave what not lessen a sense of overall satisfaction, joy, and excitation. To some extent, anxiety, anger, and rarely fear, shape the cave tourist experience. Management implicationsThe tourism product of any kinds of tourism should meet the expectations and needs of their participants. Dedicated speleotourists visiting sites outside mass tourism in popular show caves are leading by the complex of motivations typical for tourism as such. However, in the study analysed, the search for beauty and aesthetic impressions next to experiencing the unknown and novelty are explicitly significant motivators for cave exploration, even more important than looking for sport challenges and physical effort. This observation underlines cognitive and emotional meaning, not only sport character of cave exploration.The research might suggest the general direction in preparation of the offer for dedicated cave tourists: they do not require the creation of new infrastructure (e.g. in terms of cave accessibility) or provision of new amenities, but possibilities of the improvement of technical skills, while enjoying the beauty of the caves, with maintaining a balance between commercialization and the sense of uniqueness and exclusivity of the product.As pristine and unexplored caves constitute a ‘non-renewable resource,’ thus there is a need for redefinition of ‘the unknown’ and ‘the new’ concepts in cave tourism practiced by dedicated cave tourists (far beyond the geographical sense). The result of this procedure might help meet the expectations of dedicated cave tourists toward their tourist experiences and the attractiveness of sites.The explorations of dedicated cave tourists occur outside or on the fringes of tourism. To include them in the mainstream of broader tourism, it is necessary to find a balance between commercialization and the uniqueness and exclusivity of the product. The latter should provide satisfaction both in the area of physical well-being and the emotional sphere (sense of community, strengthening bonds).The study revealed the literature gap in terms of existential authenticity and the recognition of aesthetic and bodily experiences in cave tourism outside of popular show caves.

GeGra: Approaching a generic model for quantitative grain size analysis from materials microscopy data using deep learning

Grain size has a significant impact on the properties of materials, and is crucial for predicting material properties. Traditional grain size measurement relies heavily on human operators, leading to subjective results, and existing machine learning methods are typically material-specific, requiring significant labeling and training efforts for each new material. This paper provides insight into developing a deep learning-based generic grain boundary detection model (GeGra) from different material micrographs. The model is trained on 1006 images from various microscopy techniques such as light optical, Kerr, and scanning electron microscopy, acquired at different magnifications for different materials such as copper, austenite, brass, sintered hard magnet, hard metal, bronze, nickel silver, and aluminum. The developed GeGra model effectively handles visual artifacts and substructures such as twin grains, which often pose challenges for material-specific, state-of-the-art grain boundary segmentation models. The developed model achieved an IoU score of 69 % on a diverse test set and enables accurate grain size analysis using external image analysis software in less than one minute, according to ASTM standards, which is more than 5 times faster than the manual method. The developed model prioritizes generality with objective that it can have broader applicability for various materials instead of high-precision grain boundary detection. Additionally, the model has the potential to be a foundational tool for generalized grain size analysis in material microscopy, reducing the effort required for such analysis and assisting both material science experts and machine learning users.

Research on the Communication Effect of Digital Collection in Museum Based on the Analysis of Review Text

Museum digital collections have become a new form of museum communication, stimulating the public's interest in and love for cultural relics, and better promoting the dissemination of culture. This paper takes China's largest digital collection selling platform - whale probe as an example, selects the digital collections that have been on the hot search list of literature and culture and blogs for at least one consecutive month in the platform, grabs the largest sample size that can be grabs with the help of Fiddler and Python to analyze it, and applies text analysis to analyze the information dissemination effect of the digital collections of museums in terms of the depth of communication. The information dissemination effect of digital collections was analyzed in terms of communication depth. It is found that external image identity, internal emotional identity and economic value identity affect the identity of the audience group from the depth of communication.

Computer vision based cargo boxes pose adjustment system for two-dimensional conveyor platform

For enhancing the accuracy of cargo box position and angle recognition on the conveyor platform, this paper proposes a cargo box attitude detection and adjustment method based on instance segmentation and image processing. This approach involves generating Mask data through target detection of the cargo box using Mask R-CNN. Using image processing algorithms to generate a minimum rectangle according to the Mask data, and the minimum rectangle data is aligned with the Bbox data of Maks R-CNN. The position and angle of the cargo box are detected based on the minimum rectangular data, and the conveyor platform is adjusted to control the cargo box attitude using the Bbox data. Nine attitude acquisition and comparison experiments were conducted on the cargo box using an angle sensor, and the deviation of the method was consistently <0.6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.6^{\\circ }$$\\end{document}, with a relative error of 1.27% for the nine total changes. Importantly, the image processing technique in this study avoids external image processing, reducing ambient light impact and enhancing cargo box recognition and attitude feedback functionality on the conveyor platform. Throughout the entire cargo box adjustment experiment, the cargo box’s can be stabilized to reach the set angle.

Research on the Development of Creative Innovation Industry of Landmark Building and City Image

Landmark building is the external image and social card of a city, which reflects the traditional culture and spiritual connotation of a city and even a country. Landmark buildings not only refer to the large modern buildings in the city, but also include large sculptures, artworks, historical relics and so on. The innovative and creative industries of landmark buildings and urban images are often attached to the cultural tourism industry. At present, the development and research in this field are vague in definition, and the industrial development situation varies significantly in different cities and regions. Domestic first-tier cities such as Beijing, Shanghai and some tourist cities such as Hangzhou and Suzhou have advanced and comprehensive development in this field, and there are also unconscious spontaneous behaviors of the public to broaden the research content in this field. In contrast, some cities have not yet formed a good industry in this field. The development of innovative and creative industries for landmark buildings in foreign countries is earlier than that in China, and its forms are more abundant and avant-garde, which is worth learning and reference for researchers. The development of innovative and creative industries in landmark buildings will enhance the image and popularity of the city, thus boosting the development of the local tourism economy. At the same time, the industry shows the meaning and connotation of landmark buildings by means of artistic expression. In addition, innovative and creative products will also promote the popularization of arts education for all. Through a series of research and practice, the author also adds some specific cases and feasible research routes for the research and development of this field.

What motivates residents’ approval of hosting another winter mega-sporting events?

ABSTRACT This research aimed to examine the relationships between perceived social impacts and residents’ attitudes towards hosting another winter mega-sporting event depending on the level of event-related behaviours. An online survey was conducted with residents of Sapporo a week after the closing ceremony of the 2017 Sapporo Asian Winter Games. A total of 639 samples were included in a three-step hierarchical multiple regression analysis. The results showed that External image enhancement, Consolidation & Pride and Interest in sports perceived by the Winter Games hosting had positive effects on improving attitudes towards hosting another mega-sporting event, whereas Economic cost was negatively associated with the attitudes. Moreover, such positive effects were differently perceived according to the residents’ frequency of following the 2017 Sapporo Asian Winter Games through media.

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.

The Kočevje Region Between the Hammer And the Anvil: a History of Political Experimentation

The article presents the historical development of the Kočevje region in light of political experimentation from the Second World War to present day. The core of the empirical study is the period between 1991 and 2021, when the Kočevje region confronted economic decline and social turmoil, which were a consequence of past political decisions. We studied the available literature and statistical data and conducted ten interviews with key local stakeholders, based on which we then identified three periods of the contemporary development of the Kočevje region. The post-independence transition (1991–2010) marked the destruction of industrial companies, the non-responsiveness of the local environment, and the overall stagnation in development. The establishment of new local government (after 2010) brought about a developmental shift, the economic revival, and a change in mentality. The current period (after 2016), on the other hand, represents the arrival of an established international company and an improvement in the external image of the Kočevje region.

Narration in the novel ”Soad and the military” by Najm Wali

This research deals with addressing the character in the novel (Suad and the Military) by the expatriate Iraqi novelist (Najm Wali)، who is considered one of the well-known novelists in the Arab world in general and in Iraq in particular، especially outside the Arab world. The research plan is based on three types of characters: (the main character)، (the secondary character)، and (the developing character)، and is preceded by an introduction to the novelist character، as it is one of the most important and important narrative components in the novel، as it specializes in the study of characters from The external and internal aspect and the effective role that it possesses to reach the detection of various aspects، such as the psychological aspect and to clarify the various individual differences، by distinguishing between the external physical image، and the internal psychological image، and through the novelist's description of it. And its role in taking positions in order to reach a complete picture of it and its definition in the linguistic perspective، and then the terminology، followed by the most important results reached by the research، and the references used in it. * Najim Wali، born in 1956، was born in the city of Al-Amara، left Iraq in 1980 and now resides in Germany. He holds a BA in German literature from the German (Hamburg University) and Spanish literature at (Completense University) in Madrid. Concordia) in (2018)، and his novels: (The War in the Tarab neighborhood) in (1989)، (A place called Kumait) in (1997)، (Tal Lahem) in (2001)، and (The Picture of Youssef) in (2005). )، (Angels of the South) (2009)، (Baghdad Malboro) (2012)، (Sarah’s Sin) (2018)، and two short story collections: (Mary’s Last Night) (1994)، and (Waltz with Matilda) (1999)، (The Book of Baghdad، a Civilian Biography) in (2015)، and finally his novel (Suad and the Military)، which is his last work، as it was published last year.

The Effect of Personal Color Perception on Makeup Behavior of Female College Students in Their 20s

In the modern era, as the era of external image communication, interest in appearance has become an essential factor in success. As such, many people are investing a lot of time and money in appearance management to create an ideal image. In particular, personal color occupies a large proportion as a field of image management. The purpose of this study was to find out the personal color recognition and the perception of makeup behavior according to personal color in female college students in their 20s. As a result of the study, the overall personal color recognition and makeup behavior of the pre-survey group were high. Direct recognition, preference recognition, and external advice by sub-factors of personal color recognition of the pre-survey group were found to have a statistically significant positive (+) correlation with aesthetics, fashionability, interest, co-movement, courtesy by sub-factors of makeup behavior. As a result of the pre-post analysis of the subject’s personal color recognition, it was found that the post-mortem of personal color recognition was higher than before, showing a statistically significant difference. This study was intended to empirically investigate whether it affects personal color perception and makeup behavior, self-esteem, and interpersonal relationships among female college students in their 20s, and present it as basic data necessary to enhance the changed social atmosphere and lead beauty trends. The limitation of this study is that it is difficult to generalize the results only to 155 female college students at K University, so subsequent studies should expand the region, research subject, and major.

Exploration of the Digital Path of External Image Shaping of High-pitched Mountain Songs in Eastern Sichuan in the Era of Intelligent Media

In the era of intelligent media, the boundaries of information dissemination have been infinitely broadened, and the protection and inheritance of traditional culture are facing unprecedented opportunities and challenges. As a bright pearl in the cultural treasures of Bashu, the high-pitched mountain song of eastern Sichuan urgently needs to shine with new brilliance on the stage of globalization with its unique artistic charm and profound historical heritage. The purpose of this paper is to explore how to use digital means to create the unique image of high-pitched folk songs in eastern Sichuan Province in the context of the era of intelligent media, and to give wings to this intangible cultural heritage through the deep integration of technology and culture.

Image to Label to Answer: An Efficient Framework for Enhanced Clinical Applications in Medical Visual Question Answering

Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications.

Correction for geometric distortion in the flattened representation of pipeline external surface

Conventional cameras often produce external pipeline surface images with significant perspective-projection distortions, making determining the type and size of defects on the pipeline surface difficult. To address this issue, we present a novel approach for correcting the geometric distortion of a constant diameter pipeline (CDP) external surface image based on apparent contour pairs (ACPs). First, the camera is calibrated and the real projection position of ACPs accurately extracted. Second, a single-view perspective projection model of the CDP is adopted to realize a three-dimensional reconstruction of the pipeline. Subsequently, the flattened image of the CDP’s outer surface is obtained by performing equidistant sampling of cross sections, determining the visibility of discrete points, and mapping projection points onto a flat surface. Simulations and real experiments demonstrated the high accurate method in the correction of CDP images. Experiments conducted under different outdoor scenarios confirmed the robustness of the proposed method.

Handwritten Text Recognition in Machine Learning

In this machine learning project, we will recognize handwriting text (single English alphabet) this we are going to achieve by modelling a neural network that will have to be trained over a dataset containing images of alphabets. And used CNN algorithm for external input image prediction

Impact of Telemedicine on Blood Glucose Control and Ophthalmic Screenings for Patients with Diabetes in Remote Areas During the COVID-19 Pandemic: A Real-World Study in Northern Taiwan.

Introduction: The purpose of this study was to assess the impact of telemedicine on ophthalmic screening and blood glucose control for patients with diabetes in remote areas of Northern Taiwan during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Telemedicine was implemented in Shiding and Wanli Districts using a 5G platform from April 2021 to December 2022. Patients with poorly controlled diabetes received real-time consultations from endocrinologists at Far Eastern Memorial Hospital, 50 km away, for medication adjustment, diet control, and lifestyle recommendations. The study also provided cloud-upload blood glucose meters for self-monitoring and regular medical advice from hospital nurses. Ophthalmic screenings included fundus imaging, external eye image, and intraocular pressure measurement, with instant communication and diagnosis by ophthalmologists through telemedicine. A satisfaction questionnaire survey was conducted. Results: The study enrolled 196 patients with diabetes. Blood glucose and glycosylated hemoglobin levels were significantly reduced after applying telemedicine (p = 0.01 and p = 0.005, respectively). Ophthalmic screenings led to hospital referrals for 16.0% with abnormal fundus images, 15.6% with severe cataract or anterior segment disorders, and 27.9% with ocular hypertension or glaucoma. Fundus screening rates remained high at 86.3% and 80.4% in 2022, mainly using telemedicine, comparable with the traditional screening rate in the past 5 years. The overall satisfaction rate was 98.5%. Conclusions: Telemedicine showed effectiveness and high satisfaction in managing diabetes and conducting ophthalmic screenings in remote areas during the COVID-19 pandemic. It facilitated early diagnosis and treatment of ocular conditions while maintaining good blood glucose control and fundus screening rates.

Smučanje in podobe spola v dvajsetih in tridesetih letih 20. stoletja

During its pioneering years at the turn of the 20th century, skiing in Slovenia, much like sports in general, was primarily recognised as a field in which the patriarchal social order was being consolidated. Thus, the skiing world was initially dominated by men, though the gradual inclusion of women shows that skiing was not an exclusively male activity. However, the presence of women in skiing was limited and defined by traditional societal expectations and prejudices. Individual forms of skiing – such as competitive cross-country skiing, long-distance running, or ski jumping – became completely established as building blocks of social images that characterised and cultivated the male gender, while, on the other hand, alpine disciplines in particular were much more acceptable for women. The female images of skiing were strongly associated mainly with the search for moderation in these activities and the external image of the participants (fashion). Meanwhile, the circles that dictated the development of skiing in Slovenia saw women primarily in the role of educators.

Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.