2026 August - 64 articles
Landmark recognition for smart tourism is usually validated on curated benchmark images. In deployment, however, the classifier must handle user-generated photographs whose viewpoint, lighting, resolution, occlusion, and compression differ sharply fr...
Fine-grained poisonous mushroom recognition in natural scenes is challenging because of complex backgrounds, subtle morphological differences, and the limited interpretability of model decisions. To address these challenges, this paper proposes Att-F...
A reliable sign of the absence of a document presentation attack, when a print copy is presented instead of the original document, is the presence of such security features as OVDs (Optical Variable Devices), for example, holograms. To check the pres...
The conventional parallel seismic test (PST) is a widely used and highly reliable method for determining lengths of in-service bridge pile foundations. However, it relies solely on the manual interpretation of first-arrival in seismic records and oft...
Red–green–blue and depth (RGB-D) semantic segmentation combines appearance cues from RGB images with geometric information from depth maps, but sensor noise, missing measurements, and boundary-inconsistent depth responses can introduce co...
Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single...
Radiomics features are strongly sensitive to image acquisition, and separating that sensitivity from biological signal usually requires repeated patient scans that cannot be shared. We present an open, fully synthetic framework (radiomics-phantom) th...
Medical image resolution plays a crucial role in early disease detection and fine-structure observation. Super-resolution reconstruction technology can restore low-resolution images to high-resolution versions, thereby assisting physicians in making...
Patients with implanted cardiac devices are a rapidly growing imaging population, and electrocardiogram-gated cardiac computed tomography (CT) is increasingly used to characterize device geometry, multi-device relationships, and dynamic behavior acro...
Due to the limited dynamic range of imaging sensors, most cameras can only capture low-dynamic-range (LDR) images. Multi-exposure fusion (MEF) is an effective technique for generating high-dynamic-range (HDR) images. However, to simultaneously preser...
Lumbar foraminal stenosis grading contributes to surgical-level selection, but automated four-grade classification remains challenging. Published pipelines for this dataset reach approximately 65% four-class accuracy, and deep classifiers offer no an...
A transient ischemic attack (TIA) is a transient episode of neurological dysfunction without evidence of acute infarct demarcation on neuroimaging. The radiographic features of TIAs are uncertain, as since there is currently no perfect imaging method...
Aviation ground safety and protective devices are critical for flight safety; however, their unintentional retention on aircraft after maintenance remains a persistent risk. Existing deep learning-based approaches for aviation safety have predominant...
Non-rigid image registration is a fundamental problem in medical imaging and a representative example of continuous transformation modeling in image processing. Diffeomorphic registration methods, such as Large Deformation Diffeomorphic Metric Mappin...
Automated lumbar computed tomography (CT) Hounsfield unit (HU) measurement can support opportunistic osteoporosis screening, but heterogeneous Digital Imaging and Communications in Medicine (DICOM) inputs often disrupt automated workflows before meas...
Systematic optimization of pediatric brain computed tomography (CT) protocols remains challenging because clinical data are limited for children younger than 5 years. Sixty-nine non-contrast brain CT examinations in children aged 5 years or younger w...
Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and...
Histopathology foundation models (FMs) have become widely used as patch-level feature extractors in computational pathology (CPath), where domain shift is a central challenge, yet their generalization ability across acquisition centers and scanning p...
Pre-wash sorting of medical textiles is essential for hospital infection control, yet accurate stain detection remains challenging because visually apparent stains often have blurred boundaries, whereas dried urine stains lack distinguishable optical...
Osteoarthritis (OA) causes irreversible cartilage damage, highlighting the need for early and sensitive assessment. Current imaging modalities are limited in detecting early-stage changes. Ultrasound (US) provides a non-invasive and accessible altern...
Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage e...
Vision Transformers, especially Swin Transformer, have become default backbones for various vision tasks but suffer from high memory consumption and training costs. This letter proposes MoR–Swin, a novel architecture that integrates Mixture of...
Remote sensing change detection (RSCD) has advanced through convolutional, attention-based, transformer, and hybrid architectures, yet models are commonly compared as whole architectural families rather than by how their temporal streams interact. Th...
Accurate segmentation of skin lesions and gastrointestinal polyps is essential for early diagnosis and treatment planning. Currently, Convolutional Neural Networks (CNNs) are limited by local receptive fields, missing small lesions. While Transformer...
Deep learning-based surgical vision systems achieve strong performance in semantic segmentation and phase recognition, but their black-box nature limits traceability in safety-critical clinical settings. Prototype-based networks offer an interpretabl...
Virtual reality-based subjective visual vertical (VR-SVV) has attracted attention as a potential solution to equipment-related limitations of conventional SVV testing. This study investigated the test–retest reliability and adequate trial numbe...
Color texture classification is an important task in computer vision, with applications in medical imaging, industrial inspection, remote sensing, and material analysis. This paper presents a hybrid framework that integrates Principal Component Analy...
In the original publication [...]
With the rapid development of three-dimensional (3D) sensing technologies, multimodal 3D model classification has achieved significant progress. However, most existing methods are developed under closed and balanced assumptions, which limits their ap...
Three-dimensional (3D) imaging systems, including depth cameras, LiDAR sensors, and multi-view scanning pipelines, often produce point clouds with noisy normals, outliers, sparse sampling, and non-uniform density, which can degrade downstream mesh re...
In recent years, there has been increased attention on classifying histopathological images through hybrid decision-level fusion, and the challenge of exploring data fusion to improve classification accuracy in colon cancer has become significant. Th...
Whole-body magnetic resonance imaging (WB-MRI) has become established for selected clinical indications and is increasingly studied across many fields including but not limited to oncologic, musculoskeletal, pediatric, and computational imaging appli...
Semantic communication provides a task-oriented alternative to conventional bit-level transmission. For wireless image transmission, existing systems mainly optimize image reconstruction, while downstream classification is often handled by a separate...
Accurate segmentation of non-small cell lung cancer (NSCLC) on positron emission tomography/computed tomography (PET/CT) is an essential prerequisite for automated metabolic tumor volume (MTV) quantification and staging. Although deep learning models...
Rheumatoid arthritis (RA) frequently involves the wrist joints, where early inflammation may induce subtle changes in carpal spatial architecture. This study investigated whether baseline (BL) ultrasound inflammation is associated with longitudinal c...
Synthesizing multi-phase contrast-enhanced CT images from non-contrast CT (NCCT) may provide complementary phase-specific cues for preliminary assessment. After rigorous clinical validation, such images could provide clinical decision support or aid...
Self-supervised monocular depth estimation is challenging at night because adverse illumination degrades the visual cues required for correspondence estimation and depth inference. Although appearance compensation and domain transfer can mitigate nig...
Accurate mammographic mass segmentation is crucial for computer-aided breast cancer diagnosis, but remains challenging because masses may be small, low-contrast, irregularly shaped, and partially obscured by dense fibroglandular tissue. Although rece...
Facial expression recognition technology is vital for security, verification, and personalization, but it faces challenges due to variations in scale, illumination, occlusion, and facial expressions. This paper presents a hybrid architecture that com...
Corrosion remains one of the principal causes of degradation in metallic structures across a wide range of industrial sectors. Although visual inspection is routinely employed for preliminary corrosion assessment, its effectiveness depends heavily on...
Urban tree mapping is necessary in environmental sustainability and climate change mitigation, and it depends heavily on the individual tree recognition and canopy segmentation to analyze city green cover. This systematic review discusses recent deve...
With the rapid advancement of sensor technologies, automated sign language recognition (SLR) has emerged as a critical enabler of inclusive communication systems for individuals with hearing and speech impairments. Although substantial research effor...
Neonatal echocardiography remains an essential imaging modality for the assessment of congenital and hemodynamic cardiovascular abnormalities in critically ill neonates. Recent advances in quantitative ultrasound biomarkers, deformation imaging, volu...
Wide-area building-structure mapping from long-range airborne LiDAR requires image products that can represent building footprints, roof-height variations, and structural discontinuities with low computational latency. Airborne streak-tube imaging Li...
Animal re-identification (Re-ID) asks whether two images show the same individual, a recognition task that fits naturally into applications such as reuniting lost pets with their owners. Existing methods report strong scores, but typically under a si...
Infrared target recognition plays an important role in many real-world applications, but its performance is often constrained by the scarcity of annotated infrared data. To alleviate this issue, optical-to-infrared image translation has been widely e...
Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small t...
Image processing and computer vision continue to play transformative roles across science, engineering, healthcare, transportation, agriculture, manufacturing, environmental monitoring, and intelligent systems [...]
Facial expression recognition in the wild is challenged by both noisy supervision and degraded visual evidence: subtle expression cues must be interpreted under blur, contrast changes, image noise, and annotator disagreement. Existing noisy-label FER...
In this paper, an approach to automating the monitoring of compliance with regulated technological operations using computer vision methods is proposed and investigated, with the loading of petroleum products considered as a case study. A distinctive...
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