Visual Images Research Articles

Neurosurgical applications of the exoscope: from in vitro studies to real-life surgical use in selective dorsal rhizotomy

BackgroundThe microscope has been the gold standard in neurosurgical practice due to its ability to magnify anatomical structures. However, it has limitations, including restricted visual fields and ergonomic challenges that can lead to surgeon fatigue and musculoskeletal issues. The exoscope is an emerging technology that may address these limitations by offering comparable magnification with improved ergonomics. MethodsThis study compares the traditional microscope (KINEVO 900) with a 3D digital exoscope (Aeos Digital Microscope) in visual field width, image sharpness, and ergonomic impact. Visual field assessments were conducted using millimeter paper at a fixed distance, while image sharpness was evaluated using graph paper with pins at different depths. Ergonomic evaluation involved simulating surgical positions using a spine anatomical model. The practical applicability was tested during Selective Dorsal Rhizotomy (SDR) procedures, comparing the surgeon's experience with both devices over 20 consecutive cases. ResultsThe exoscope provided a larger visual field (81.18 cm2) compared to the microscope's (54.10 cm2). Image sharpness was similar for both devices across various depths and zoom levels. Ergonomically, the exoscope allowed the surgeon to maintain a neutral posture while visualizing extreme angles, unlike the microscope, which required significant upper body movement. In SDR procedures, the exoscope improved surgeon comfort and interaction with the operating team, despite an initial learning curve. ConclusionsThe exoscope presents notable advantages in terms of visual field and ergonomics. The exoscope’s ability to facilitate better posture and team communication without compromising image quality makes it an addition to neurosurgical practice, as in SDR.

Cr3+-doped Na2CaSn2Ge3O12 garnet showing broadband near-infrared emission at 820 nm with zero thermal quenching and near-unity quantum yield

The continually advancing near-infrared (NIR) phosphor-converted light-emitting diode (pc-LED) hold pivotal significance in optical imaging and NIR spectroscopy. Cr3+-doped garnet broadband NIR phosphors have attracted considerable attention owing to their high optical performance enabled by the rigid structure. Nonetheless, the emission wavelength is severely restricted, and longer wavelength emissions usually result in inferior quantum efficiency and poor luminescence stability. In this work, the concurrent reconstruction of dodecahedral and octahedral sites by introducing [Na+-Sn4+]C unit in the framework of Na2CaSn2Ge3O12 garnet contributes to the Cr3+ broadband NIR emission spanning 700–1200 nm, with a peak at 820 nm. Utilizing a charge compensation strategy via Ta5+ codoping largely boosts the internal quantum efficiency (IQE) of Na2CaSn2Ge3O12:Cr3+ from 93.06 % up to 95.04 %. Trap-mediated energy compensation is proposed to achieve a record-breaking zero-thermal-quenching behavior (99.39 %@423 K) of Cr3+-doped garnet with such a longer wavelength emission. The encapsulated prototype broadband NIR pc–LED gives rise to an overwhelming NIR output power of 590.9 mW@1300 mA and a photoconversion efficiency (PCE) of 32.62 % at 30 mA, rendering exceptional performance in night vision, detection, and NIR imaging applications.

The connection of mise-en-scene camera movement in the visual image to cinematic decoration as elements of scenography Applying to the movie “Kira and the Jinn”

The connection of mise-en-scene camera movement in the visual image to cinematic decoration as elements of scenography Applying to the movie “Kira and the Jinn”

Robust visual question answering via polarity enhancement and contrast

The Visual Question Answering (VQA) task is an important research direction in the field of artificial intelligence, which requires a model that can simultaneously understand visual images and natural language questions, and answer questions related to images. Recent studies have shown that many Visual Question Answering models rely on statistically regular correlations between questions and answers, which in turn weakens the correlation between visual content and textual information. In this work, we propose an unbiased Visual Question Answering method to solve language priors from the perspective of strengthening the contrast between the correct answer and the positive and negative predictions. We design a new model consisting of two modules with different roles. We input the image and the question corresponding to it into the Answer Visual Attention Modules to generate positive prediction output, and then use a Dual Channels Joint Module to generate negative prediction output with great linguistic prior knowledge. Finally, we input the positive and negative predictions together with the correct answer to our newly designed loss function for training. Our method achieves high performance (61.24%) on the VQA-CP v2 dataset. In addition, most existing debiasing methods improve performance on VQA-CP v2 dataset at the cost of reducing performance on VQA v2 dataset, while our method not only does not reduce the accuracy on VQA v2 dataset. Instead, it improves performance on both datasets mentioned above.

Multiple description image coding using Schur transform

A novel obvious and robust image Multiple Description Coding (MDC) using Schur transform (ST)is presented in this work. To overcome the inherent drawbacks in the discrete wavelet transform (DWT)which are the hardware implementation and computational complexityIn the MDC approach, two or more correlated descriptions of the same data aregenerated, which can be independently decoded, and yield mutually refinable information. Therefore, the quality of the recovered signal is dependent only on the number of received descriptions, and not on the specific loss pattern. The new techniquewas analyzed and compared to the conventional approach in the case of four descriptions by using multiple grayscale test images having different spectral characteristics.Theexperimental results of Schur transform (ST) compared to discrete wavelet transform (DWT)show that our method achievesbetter performance in terms of delivered peaksignal to noise ratio (PSNR) and visual image quality reconstruction.The proposed MDC has a low redundancy and outperforms the conventionalscheme for image transmission over error-prone networks.Therefore, the proposed approach provides an easier practical implementation and a better redundancy performancethan the classical approach.

Achieving Flat Ultra‐Broadband NIR Emission in Cr3+ Doped Garnet‐Type Phosphors Enabled by Structural Regulation Toward Multi‐Functional Spectroscopy Applications

AbstractCr3+‐activated near‐infrared (NIR) emitting phosphors are considered as one of the most potential light‐conversion materials for NIR phosphor converted light‐emitting diodes (NIR pc‐LEDs). However, it is still a challenge for a single Cr3+ ion to achieve a flat ultra‐broadband emission toward multi‐functional spectroscopy applications. Herein, a chemical unit co‐substituting strategy is utilized to regulate the crystallographic occupancy of Cr3+ ions, and a flat ultra‐broadband NIR emission is successfully realized with a record emission wavelength of 915 nm in garnet‐type phosphors Ca3Sc2‐xHfxAlxSi3‐xO12: yCr3+ (0 ≤ x ≤ 1, 0.02 ≤ y ≤ 0.06), together with a large full width at half maximum (FWHM) regulation from 130 to 250 nm. The relation between the site occupation of Cr3+ ions in disordered [CaO8], [(Sc, Hf)O6] and [(Si, Al)O4] polyhedrons and the corresponding NIR emission are clarified by carefully evaluating the structural evolution, Raman spectra, electron paramagnetic resonance and time‐resolved emission lifetime spectra of Cr3+ ions. The corresponding crystal field strength of Cr3+ ions is investigated to further clarify the multi‐sites induced flat broadband NIR emission. Finally, a blue light pumped NIR pc‐LED is fabricated with a total output power of 37.58 mW and photoelectric conversion efficiency (PCE) of 13.67%@100 mA, which realizes the multi‐functional applications in night vision imaging, non‐destructive detection and palmprint vein recognition for assistant medical treatment.

Cruising Europe in World War II: Epistemological Challenges of German Vernacular Trophy Photography

The Nazi regime outfitted the Wehrmacht with the best photographic technology and its soldiers were far better equipped than their allies or nemeses. More than ten per cent of the eighteen million German combatants possessed a fast-shutter 35-mm camera. German soldier-photographers documented their wartime activities extensively, in turn leaving behind a gigantic, private and largely underexplored archive that poses multiple epistemological and ethical challenges. Military historians and Holocaust scholars approach soldierly photographs primarily as sources of information, including proof of crimes. This article shifts these lenses and argues that reading them as visual images, and not solely as historical sources and circulating artefacts, reveals a medium that equally affects its practitioners as well as the observers. Drawing upon public archives and private collections, I focus on highly performative images of victory and conquest over enemy countries and peoples and conceptualise them as ‘trophy photographs’. I examine recurrent motifs within this genre, in particular sexual encounters with enemy women and photographs of male bonding, which arguably constitute the most prevalent images snapped by soldier-photographers who ‘cruised’ Nazi-occupied Europe. Borrowing from Queer Studies, the concept of cruising allows me to probe soldiers’ mobility in semipublic spaces and also acknowledge the voyeuristic and visually alluring quality of their photographs. Seductive, exciting, these trophy photographs lure in their audience(s) with sexually charged, often (homo)erotic overtones. In turn, these images provoke diverse ways of seeing and interpreting their content: how can we best frame the soldier-photographer’s gaze and capture the polysemic quality of the images, especially when dealing with violence, racism, sexism and/or homoeroticism? What effect do trophy images of conquest potentially have on the historical actors who shot and circulated those photographs and what impact do they have on contemporary observers? Finally, acknowledging multidirectional and fractious lives of images, how do we understand the complex relationship between public history and the ethics of seeing? Because photographic images are historically contingent and open-ended, this article casts light on the complexity of mediation and competing visions between diverse publics of the past, present and future.

A Practical Guide to Loco-Regional Nerve Blocks for Oromaxillofacial Surgery in Dogs and Cats.

This article provides an overview of the neuroanatomy of the head with a detailed explanation and visual images to enable accurate placement of loco-regional nerve blocks to achieve pre-emptive blockade of the nociceptive input which occurs when performing oral surgery. Variations in anatomy and between species are addressed to assist in accurate placement.

Analysis of the Impact of Employees' Own Indicators on Employee Mobility

Abstract. Nowadays, the enterprise employee turnover rate has become an important factor in measuring enterprise knowledge loss, human resource management, enterprise comprehensive competitiveness, and enterprise expenditure. Therefore, as much as possible may affect the enterprise employee turnover data and analyze the influence on employee turnover degree can maintain stable staff and increase its comprehensive competitiveness in the market economy system. Employee mobility is not only affected by corporate and macro environmental factors, such as corporate culture, incentive system, employee relations, economic environment, but also related to employees' factors, such as career development planning, job satisfaction, salary, and burnout. Research on employee mobility and its impression factors should be carried out from multiple aspects and perspectives. This paper refers to the relevant literature on the impression factors of employee mobility in the past, combines the basic data that may affect the employee turnover rate to analyze the Impact of employees' indicators on employee mobility, pre-processes the data, and presents the visual image of the data. And then the paper uses multicollinearity diagnosis to exclude the collinearity influencing variables and conduct a preliminary correlation analysis of the variables. Finally, this text establishes a regression analysis model of the related variables, obtains seven variables related to employee mobility, and gives reasonable suggestions for the enterprise research employee turnover rate.

AEFF-SSC: an attention-enhanced feature fusion for 3D semantic scene completion

Abstract Three-dimensional (3D) occupancy perception technology aims to enable autonomous vehicles to observe and understand dense 3D environments. Estimating the complete geometry and semantics of a scene solely from visual images is challenging. However, humans can easily conceive the complete form of objects based on partial key information and their own experience. This ability is crucial for recognizing and interpreting the surrounding environment. To equip 3D occupancy perception systems with a similar capability, a 3D semantic scene completion method called AEFF-SSC is proposed. This method deeply explores boundary and multi-scale information in voxels, aiming to reconstruct 3D geometry more accurately. We have specifically designed an attention-enhanced feature fusion module that effectively fuses image feature information from different scales and focuses on feature boundary information, thereby more efficiently extracting voxel features. Additionally, we introduce a semantic segmentation module driven by a 3D attention-UNet network. This module combines a 3D U-Net network with a 3D attention mechanism. Through feature fusion and feature weighting, it aids in restoring 3D spatial information and significantly improves the accuracy of segmentation results. Experimental verification on the SemanticKITTI dataset demonstrates that AEFF-SSC significantly outperforms other existing methods in terms of both geometry and semantics. Specifically, within a 12.8 m × 12.8 m area ahead, our geometric occupancy accuracy has achieved a significant improvement of 71.58%, and at the same time, the semantic segmentation accuracy has also increased remarkably by 54.20%.

Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan

BackgroundThe early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of...

Effect of Visual Images on Digital Communication: Empirical Research Review

As mobile messengers gain popularity, visual images affect digital communication, leading to new interaction patterns. This review sums up scientific experience in describing digital communication facilitated by emoji and memes. The review covered scientific articles that reported empirical research on visual communication in digital environment in such open research databases as CyberLeninka, PubMed, Google Scholar, eLibrary, and ResearchGate in 2019–2024. The current rise in visual communication tools seems to reflect the general visual shift in modern culture, associated with the changes in human psychology brought about by total digitalization. Visual communication tools have a high information capacity, which hinders their interpretation, thus modifying the complex of individual and socio-psychological characteristics of interlocutors in an attempt to ensure mutual understanding. The review can be used to improve digital communication that employs alternative communication tools.

Self-Organized Operational Neural Networks for Early Glaucoma Diagnosis in Digital Fundus Images

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.

Maximizing Potential : 5W+1H Reading Comprehension Strategy for Student with Moderate Intellectual Disability

This study aims to enhance the reading comprehension skills of elementary school students with moderate intellectual disabilities through a single-subject method. In this study, the researchers use the 5W + 1H strategy to help students understand the meaning of the text. The research data was collected using observation guidelines, which then will be analyzed by using pretest-posttest techniques to assess the subject’s reading comprehension skills improvements. Teaching the 5W + 1H principle - which emphasized the question words why and how using visual images and keywords - was found to be effective in enhancing the subject’s reading comprehension. This finding also explained that this method could maximize the potential of students with moderate intellectual disabilities. The instruction on the question words why and how was particularly effective in increasing the subject's understanding of questions as well as leading him to provide correct and contextually appropriate responses. However, using visual images as a learning aid, especially for why and how questions, seemed less effective, as evidenced by the subject's reliance on keywords within the sentence. In the future, this strategy can be employed by educators to enhance reading comprehension skills for students with moderate intellectual disabilities.

Deep learning-powered efficient characterization and quantification of microplastics

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

Research on Target Image Classification in Low-Light Night Vision.

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen's features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

DCV2I$\text{DCV}^2\text{I}$: Leveraging deep vision models to support geographers' visual interpretation in dune segmentation

AbstractVisual interpretation is extremely important in human geography as the primary technique for geographers to use photograph data in identifying, classifying, and quantifying geographic and topological objects or regions. However, it is also time‐consuming and requires overwhelming manual effort from professional geographers. This paper describes our interdisciplinary team's efforts in integrating computer vision models with geographers' visual image interpretation process to reduce their workload in interpreting images. Focusing on the dune segmentation task, we proposed an approach called featuring a deep dune segmentation model to identify dunes and label their ranges in an automated way. By developing a tool to connect our model with ArcGIS—one of the most popular workbenches for visual interpretation, geographers can further refine the automatically generated dune segmentation on images without learning any CV or deep learning techniques. Our approach thus realized a noninvasive change to geographers' visual interpretation routines, reducing their manual efforts while incurring minimal interruptions to their work routines and tools they are familiar with. Deployment with a leading Chinese geography research institution demonstrated the potential of in supporting geographers in researching and solving drylands desertification.

“He would do nothing in the office”: Work and alienation in cinematic adaptations of Herman Melville’s Bartleby.

This paper analyzes the representation of the office workplace in filmic adaptations of Melville’s short story “Bartleby, the Scrivener”. Following a brief overview of film adaptations of Melville’s short story, this article will discuss Miro Bilbrough’s (2001) and Gerard Amsellem’s (2013) short films, Andreas Honneths (2010) experimental interpretation as well as Laura Naylor and Kristen Kee’s (2017) stop-motion movie. By, for instance, analyzing how the movies try to replicate the first-person narration, find visual images for Melville’s uncinematic descriptive prose, or underline the thematic importance of writing by incorporating different forms of written text, I will firstly examine techniques of intermedial transposition. Since the filmmakers deploy the setting to reimagine or modernize the story, the set design is to be regarded as an essential part of the adaptation process. Paying particular attention to the usage of space, the visual presentation of physical and metaphorical walls, as well as the utilization of non-human entities as ‘actors,’ I will seek to demonstrate how the movies under study reflect contemporary office environments and dynamics of work culture. Updating the nineteenth-century quills and inkpots to modern office technology, for example, allows the directors to reflect upon how electronic communication can cause or reinforce isolation. Different concepts of alienation serve as an essential theoretical framework for the movies. The adaptations concern themselves with social disconnect, engage in questions of ecological alienation, or focus on a political and socioeconomic interpretation of Bartleby’s passive resistance to productivity and consumption.

Research on the Knowledge Structure and Sustainable Development Pathways of Artificial Intelligence from the Perspective of Technological Science

Achieving significant breakthroughs in both the fundamental theories and technological applications of artificial intelligence is essential for fostering its long-term development. Under the guidance of Professor Qian Xuesen’s theory of technological science, exploring the internal mechanisms of knowledge evolution in artificial intelligence holds profound theoretical and practical significance for promoting sustainable technological advancement. This study draws on literature from the Web of Science (WOS) database and employs methods such as knowledge mapping, natural language processing, clustering analysis, and citation analysis to outline the knowledge structure of the field, clarify the trajectory of sustainable development, and trace the technological genealogy of VR/AR technologies.This study divides the knowledge structure within the field of technological science into “basic theoretical knowledge—applied basic knowledge—applied knowledge”, enriching Qian’s theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice. Artificial intelligence encompasses 10 distinct knowledge domains, among which machine learning and deep learning constitute the basic theoretical knowledge, data intelligence, computer vision, and swarm intelligence are the applied basic knowledge, and image processing and human-computer intelligence are the applied knowledge. The development of VR/AR technology has formed two main sustainable development paths: “machine learning—data intelligence—intelligent systems—human computer intelligence”, and “deep learning—computer vision—image processing”.