Search Results (2,350)

Gesture recognition has a crucial role in Human–Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper, three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable one. All methods, machine learning using IMU, machine learning on device, and were combined with certain activities that were determined during a needs analysis research. The same volunteers took part in the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that while some methods excel in specific scenarios, the integrated solution of MoveNet and CNN provides a robust framework for real-time gesture recognition. Full article

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios. Full article

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00]. Full article

The current global academic system, rooted in a reductionist, materialist and westernized worldview, reflects the modern industrial era in which it took shape and is therefore ill-equipped to address the complex challenges of today’s polycrisis. This viewpoint offers a vision for a complementary system aimed at filling this gap, one grounded on an expanded notion of what science and higher education can be and how best they can serve the world. It is part of an independent research and book project on the broad topic of Reimagining Academia, developed in dialogue with pioneering and spiritually oriented scientific and professional networks. Moving from the recognition of the principal limits of today’s universities, the paper describes an alternative home for all those scholars, students, practitioners and social constituencies whose worldviews and knowledge systems are shifting towards more holistic approaches. Grounded on a new ontological framework and on a human-centered modus operandi, the proposed system would aim to revive scientific disciplines from the inside out, by means of new life-affirming assumptions and purposes. The paper concludes by outlining practical steps for the realization of this vision, proposing a global alliance of scientific, cultural, and social actors. Full article

Human–computer interaction (HCI) with three-dimensional (3D) Building Information Modelling/Model (BIM) is the crucial ingredient to enhancing the user experience and fostering the value of BIM. Current BIMs mostly use keyboard, mouse, or touchscreen as media for HCI. Using these hardware devices for HCI with BIM may lead to space constraints and a lack of visual intuitiveness. Somatosensory interaction represents an emergent modality of interaction, e.g., gesture interaction, which requires no equipment or direct touch, presents a potential approach to solving these problems. This paper proposes a computer-vision-based gesture interaction system for BIM. Firstly, a set of gestures for BIM model manipulation was designed, grounded in human ergonomics. These gestures include selection, translation, scaling, rotation, and restoration of the 3D model. Secondly, a gesture understanding algorithm dedicated to 3D model manipulation is introduced in this paper. Then, an interaction system for 3D models based on machine vision and gesture recognition was developed. A series of systematic experiments are conducted to confirm the effectiveness of the proposed system. In various environments, including pure white backgrounds, offices, and conference rooms, even when wearing gloves, the system has an accuracy rate of over 97% and a frame rate maintained between 26 and 30 frames. The final experimental results show that the method has good performance, confirming its feasibility, accuracy, and fluidity. Somatosensory interaction with 3D models enhances the interaction experience and operation efficiency between the user and the model, further expanding the application scene of BIM. Full article

In the domain of food science, apple grading holds significant research value and application potential. Currently, apple grading predominantly relies on manual methods, which present challenges such as low production efficiency and high subjectivity. This study marks the first integration of advanced computer vision, image processing, and machine learning technologies to design an innovative automated apple grading system. The system aims to reduce human interference and enhance grading efficiency and accuracy. A lightweight detection algorithm, FDNet-p, was developed to capture stem features, and a strategy for auxiliary positioning was designed for image acquisition. An improved DPC-AWKNN segmentation algorithm is proposed for segmenting the apple body. Image processing techniques are employed to extract apple features, such as color, shape, and diameter, culminating in the development of an intelligent apple grading model using the GBDT algorithm. Experimental results demonstrate that, in stem detection tasks, the lightweight FDNet-p model exhibits superior performance compared to various detection models, achieving an [email protected] of 96.6%, with a GFLOPs of 3.4 and a model size of just 2.5 MB. In apple grading experiments, the GBDT grading model achieved the best comprehensive performance among classification models, with weighted Jacard Score, Precision, Recall, and F1 Score values of 0.9506, 0.9196, 0.9683, and 0.9513, respectively. The proposed stem detection and apple body classification models provide innovative solutions for detection and classification tasks in automated fruit grading, offering a comprehensive and replicable research framework for standardizing image processing and feature extraction for apples and similar spherical fruit bodies. Full article

The occurrence of cervical cancer is often linked to a previous infection with a human papillomavirus (HPV). In order to detect HPV infections in cervical smears, a broad range of tests can be used. This study compares the two hybridisation-based DNA-microarray systems “HPV 3.5 LCD-Array” (Chipron GmbH) and “PapilloCheck^®” (Greiner Bio-One GmbH), based on their ability to detect and differentiate HPV infections in 42 different cervical smears. PapilloCheck^® can detect and individually identify 24 HPV types, whereas the 3.5 LCD-Array can differentiate among 32 HPV genotypes. However, both systems include all 13 high-risk (HR)-classified types. With Chipron having already stopped the production of the 3.5 LCD-Array test, quite a few laboratories are confronted with the need to establish a new HPV testing method. The two methods were found to have a high agreement regarding the clinical significance of the detected HR HPV types. Discrepant cases were additionally validated with the help of a third test (VisionArray^® HPV, ZytoVision GmbH). The results of the VisionArray^® test corresponded rather well with the results of the 3.5 LCD-Array. Full article

After four decades of intensive research, traditional vaccination strategies for HIV-1 remain ineffective due to HIV-1′s extraordinary genetic diversity and complex immune evasion mechanisms. Cytomegaloviruses (CMV) have emerged as a novel type of vaccine vector with unique advantages due to CMV persistence and immunogenicity. Rhesus macaques vaccinated with molecular clone 68-1 of RhCMV (RhCMV68-1) engineered to express simian immunodeficiency virus (SIV) immunogens elicited an unconventional major histocompatibility complex class Ib allele E (MHC-E)-restricted CD8⁺ T-cell response, which consistently protected over half of the animals against a highly pathogenic SIV challenge. The RhCMV68-1.SIV-induced responses mediated a post-infection replication arrest of the challenge virus and eventually cleared it from the body. These observations in rhesus macaques opened a possibility that MHC-E-restricted CD8⁺ T-cells could achieve similar control of HIV-1 in humans. The potentially game-changing advantage of the human CMV (HCMV)-based vaccines is that they would induce protective CD8⁺ T-cells persisting at the sites of entry that would be insensitive to HIV-1 evasion. In the RhCMV68-1-protected rhesus macaques, MHC-E molecules and their peptide cargo utilise complex regulatory mechanisms and unique transport patterns, and researchers study these to guide human vaccine development. However, CMVs are highly species-adapted viruses and it is yet to be shown whether the success of RhCMV68-1 can be translated into an HCMV ortholog for humans. Despite some safety concerns regarding using HCMV as a vaccine vector in humans, there is a vision of immune programming of HCMV to induce pathogen-tailored CD8⁺ T-cells effective against HIV-1 and other life-threatening diseases. Full article

Humanity has always aspired beyond the human. The technological development in recent decades has been extraordinary, leading to new attempts to overcome the all-too-human condition. We dream of conquering death, upgrading our bodies into perfect performance machines and enhancing our intelligence through bio-nanotechnology. We are familiar with the side effects: alienation, stress, anxiety, depression. This article contends that Franz Kafka’s enigmatic oeuvre at its core harbors a yearning to transcend the human. Through a close reading of the narrative In the Penal Colony, it is demonstrated that this yearning is far more radical and uncompromising than the modern vision of extending and optimizing human life. Instead of the modern ego-concerned affirmation of life and the body that hides behind much of AI and modern technology, Kafka seeks a radical vision of total transformation and transcending the human into ‘nothingness’. The article shows that this transformation corresponds to core concepts in Arthur Schopenhauer’s philosophy, primarily his doctrine of the denial of the will to live and asceticism. Instead of the species-narcissistic affirmation of life and the body that lurks behind much of AI and modern technology, Kafka strives for a definitive overcoming of the life we desire. Full article

The transition from Industry 4.0 to Industry 5.0 gives more prominence to human-centered and sustainable manufacturing practices. This paper proposes a conceptual design framework based on Vision Transformers (ViTs) and digital twins, to meet the demands of Industry 5.0. ViTs, known for their advanced visual data analysis capabilities, complement the simulation and optimization capabilities of digital twins, which in turn can enhance predictive maintenance, quality control, and human–machine symbiosis. The applied framework is capable of analyzing multidimensional data, integrating operational and visual streams for real-time tracking and application in decision making. Its main characteristics are anomaly detection, predictive analytics, and adaptive optimization, which are in line with the objectives of Industry 5.0 for sustainability, resilience, and personalization. Use cases, including predictive maintenance and quality control, demonstrate higher efficiency, waste reduction, and reliable operator interaction. In this work, the emergent role of ViTs and digital twins in the development of intelligent, dynamic, and human-centric industrial ecosystems is discussed. Full article

This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of [email protected] to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in [email protected]. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking. Full article

The development of deep learning has led to the proposal of various models for human activity recognition (HAR). Convolutional neural networks (CNNs), initially proposed for computer vision tasks, are examples of models applied to sensor data. Recently, high-performing models based on Transformers and multi-layer perceptrons (MLPs) have also been proposed. When applying these methods to sensor data, we often initialize hyperparameters with values optimized for image processing tasks as a starting point. We suggest that comparable accuracy could be achieved with fewer parameters for sensor data, which typically have lower dimensionality than image data. Reducing the number of parameters would decrease memory requirements and computational complexity by reducing the model size. We evaluated the performance of two MLP-based models, MLP-Mixer and gMLP, by reducing the values of hyperparameters in their MLP layers from those proposed in the respective original papers. The results of this study suggest that the performance of MLP-based models is positively correlated with the number of parameters. Furthermore, these MLP-based models demonstrate improved computational efficiency for specific HAR tasks compared to representative CNNs. Full article

The streptozotocin-induced rat model of diabetic retinopathy presents similarities to the disease observed in humans. After four weeks following the induction of diabetes, the rats experience vision impairment. During this crucial four-week period, significant changes occur, with vascular damage standing out as a clinically significant factor, alongside neovascularization. While redox imbalance, activation of microglia, secretion of pro-inflammatory cytokines, and neuronal cell death are also observed, the latter remains an emerging hypothesis requiring further exploration. This review is a comprehensive and up-to-date chronological depiction of the progression of diabetic retinopathy within the initial four weeks of hyperglycemia, which precede the onset of vision loss. The data are structured in weekly changes. In the first week, oxidative stress triggers the activation of retinal microglia, which produces inflammation, leading to altered neurotransmission. The second week is characterized by leukostasis, which promotes ischemia, while neural degeneration begins and is accompanied by a simultaneous increase in vessel permeability. The progression of redox and inflammatory imbalances characterized the third week. Finally, in the fourth week, significant developments occur as vessels dilate and become tortuous, neovascularization develops, and retinal thickness diminishes, ultimately leading to vision loss. Through this clearly structured outline, this review aims to delineate a framework for the progression of streptozotocin-induced diabetic retinopathy. Full article

Landscape, as an important component of environmental quality, is increasingly valued by scholars for its visual dimension. Unlike evaluating landscape visual quality through on-site observation or using digital photos, the landscape visualization modeling method supported by unmanned aerial vehicle (UAV) aerial photography, geographic information System (GIS), and PixScape has the advantage of systematically scanning landscape geographic space. The data acquisition is convenient and fast, and the resolution is high, providing a new attempt for landscape visualization analysis. In order to explore the application of visibility modeling based on high-resolution UAV remote sensing images in landscape visual evaluation, this study takes campus landscape as an example and uses high-resolution campus UAV remote sensing images as the basic data source to analyze the differences between the planar method and tangent method provided by PixScape 1.2 software in visual modeling. Six evaluation factors, including Naturalness (N), Normalized Shannon Diversity Index (S), Contagion (CONTAG), Shannon depth (SD), Depth Line (DL), and Skyline (SL), are selected to evaluate the landscape vision of four viewpoints in the campus based on analytic hierarchy process (AHP) method. The results indicate that the tangent method considers the visual impact of the vertical amplitude and the distance between landscape and viewpoints, which is more in line with the real visual perception of the human eyes. In addition, objective quantitative evaluation metrics based on visibility modeling can reflect the visual differences of landscapes from different viewpoints and have good applicability in campus landscape visual evaluation. It is expected that this research can enrich the method system of landscape visual evaluation and provide technical references for it. Full article

Most existing low-light image enhancement (LIE) methods are primarily designed for human-vision-friendly image formats, such as sRGB, due to their convenient storage and smaller file sizes. In addition, raw images provide greater detail and a wider dynamic range, which makes them more suitable for LIE tasks. Despite these advantages, raw images, the original format captured by cameras, are larger and less accessible and are hard to use in methods of LIE with mobile devices. In order to leverage both the advantages of sRGB and raw domains while avoiding the direct use of raw images as training data, this paper introduces sRrsR-Net, a novel framework with the image translation process of sRGB–raw–sRGB for LIE task. In our approach, firstly, the RGB-to-iRGB module is designed to convert sRGB images into intermediate RGB feature maps. Then, with these intermediate feature maps, to bridge the domain gap between sRGB and raw pixels, the RAWFormer module is proposed to employ global attention to effectively align features between the two domains to generate reconstructed raw images. For enhancing the raw images and restoring them back to normal-light sRGB, unlike traditional Image Signal Processing (ISP) pipelines, which are often bulky and integrate numerous processing steps, we propose the RRAW-to-sRGB module. This module simplifies the process by focusing only on color correction and white balance, while still delivering competitive results. Extensive experiments on four benchmark datasets referring to both domains demonstrate the effectiveness of our approach. Full article