Three-dimensional (3D) reconstruction using images is one of the most significant topics in computer vision and photogrammetry, with wide-ranging applications in robotics, augmented reality, and mapping. This study investigates methods of 3D reconstr...
Manual microscopy for malaria diagnosis is labor-intensive and prone to inter-observer variability. This study presents an automated binary classification approach for detecting malaria ring-form infections in thin blood smear single-cell images usin...
This study investigates the performance of image-reconstruction methods derived from coupled dynamical systems for solving linear inverse problems, focusing on how appropriate parameter selection enhances noise-suppression capability in tomographic i...
In-depth research on marine biodiversity is essential for understanding and protecting marine ecosystems, where semantic segmentation of marine species plays a crucial role. However, segmenting microscopic zooplankton images remains challenging due t...
In seismic imaging, the trace mixing process involves merging neighboring traces in seismic data to enhance the signal-to-noise ratio and improve the continuity and spatial coherence of seismic data. In regions with complex subsurface structures, cur...
With the considerable number of low-dose CT examinations performed in lung cancer screening, variations in participant positioning, scan direction, or localiser angle are likely to occur in practice. These variations are known to affect automatic tub...
The paper presents a method for generating a pseudo-panchromatic (PPAN) orthophotomosaic that is spectrally compatible with the multispectral (MS) orthophotomosaic, and it targets the fusion of unmanned aircraft system (UAS) RGB–MS orthophotomo...
Bone scintigraphy is a widely available and cost-effective modality for detecting skeletal metastases in prostate cancer, yet visual interpretation can be challenging due to heterogeneous uptake patterns, benign mimickers, and a high reporting worklo...
Diagnosis of cardiomyopathies often depends on overt phenotypic manifestations, delaying patient management. This underscores the need for population-level opportunistic screening tools using clinically indicated CT scans to detect subclinical myocar...
Three-dimensional (3D) ex vivo imaging of cleared tissue from intact brains from animal models, human brain surgical specimens, and large postmortem human and non-human primate brain specimens is essential for understanding physiological neural conne...
Talking Head Generation (THG) is a rapidly advancing field at the intersection of computer vision, deep learning, and speech synthesis, enabling the creation of animated human-like heads that can produce speech and express emotions with high visual r...
Head and neck cancer (HNC) management requires highly coordinated multidisciplinary care. Radiation Therapy Technologists (RTTs) have increasingly expanded their role beyond technical execution, contributing to patient positioning, treatment delivery...
Accurate color property measurements are critical for advancing artificial vision in real-time industrial applications. RGB imaging remains highly applicable and widely used due to its practicality, accessibility, and high spatial resolution. However...
To meet the strict non-contact measurement requirements for the assembly of aircraft engine pipelines and to overcome the limitations of the traditional three-dimensional laser scanning workflow, this study proposes an automated pipeline point cloud...
Imaging luminance measurement is increasingly used in lighting applications, but the limited dynamic range of camera sensors requires using high dynamic range (HDR) imaging methods for evaluating scenes with large luminance contrasts. This work aims...
Perceived photorealism in architectural 3D rendering is not determined solely by physical accuracy or rendering complexity but also by a limited set of visual cues that observers rely on when judging realism. This literature review synthesizes findin...
Gastrointestinal cancers account for roughly a quarter of global cancer incidence, and early detection through endoscopy has proven effective in reducing mortality. Multi-class endoscopic disease detection, however, faces three persistent challenges:...
Pediatric respiratory diseases are a leading cause of hospital admissions and childhood mortality worldwide, highlighting the critical need for accurate and timely diagnosis to support effective treatment and long-term care. Chest radiography remains...
Source camera identification on smartphones constitutes a fundamental task in multimedia forensics, providing essential support for applications such as image copyright protection, illegal content tracking, and digital evidence verification. Numerous...
Autism classification is challenging due to the subtle, heterogeneous, and overlapping neural activation profiles that occur in individuals with autism. Novel deep learning approaches, such as Convolutional Neural Networks (CNNs) and their variants,...
Accurate interpretation of chest radiographs is central to the early diagnosis and management of pulmonary disorders. This study introduces an explainable deep learning framework that integrates biomedical signal fidelity analysis with transfer learn...
The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time faci...
Reliable fault detection along transmission corridors is essential for preventing small defects from developing into long outages and costly emergency operations. This study aims to improve the field reliability of an open vocabulary vision language...
Simultaneous Localization and Mapping (SLAM) is a fundamental capability in robotics and augmented reality. However, achieving accurate geometric reconstruction and consistent semantic understanding in complex environments remains challenging. Althou...
Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), are major causes of cognitive and motor decline, yet early diagnosis remains challenging due to asymptomatic phases and limited non-invasive biom...
When capturing star images in long-exposure mode, due to the relative motion between stars and space objects and the observation camera, strip tailings with different directions and lengths will be formed, resulting in a serious decline in image qual...
Accurate identification of moso bamboo (Phyllostachys edulis) age classes is essential for effective forestry resource management, yet existing methods often struggle to achieve a satisfactory balance between accuracy and computational efficiency und...
Lensless on-chip microscopy enables high-throughput, wide-FOV imaging; however, the Bayer color filter array (CFA) in standard color sensors spatially multiplexes spectral channels, introducing sub-sampling and spectral crosstalk that degrade phase r...
Emerging digital technologies, including immersive environments (VR/AR/XR) and Vision–Language Models (VLMs), have the potential to reshape digital pathology and medical imaging. While immersive tools can enhance spatial visualization and proce...
Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis,...
Accurate identification of Alzheimer’s disease (AD)-related cellular characteristics from microscopy images is essential for understanding neurodegenerative mechanisms at the cellular level. While most computational approaches focus on macrosco...
Circular-Scan photoacoustic tomography (PAT) can provide high-resolution images of optical absorption, but its analytical reconstructions, such as delay-and-sum (DAS), are highly sensitive to scanning radius (SR) inaccuracies, which cause severe geom...
Microblind hole processing quality inspection, especially accurately identifying microblind hole contour features and precisely detecting 3D and morphological parameters, has always been challenging, especially for accurately identifying those of dif...
This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The f...
Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures....
Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesio...
UV parameterization is a fundamental step in building textured 3D models, but minimizing texture distortion and ensuring seams are placed along meaningful boundaries remains a challenge. This paper proposes UVSegNet, a novel semantic boundary-aware U...
Embedded structured light cameras have been widely applied in various fields. However, due to constraints such as insufficient computing resources, it remains difficult to achieve high-speed structured light point cloud computation. To address this i...
Bacterial wound infection poses a major challenge in trauma care and can lead to severe complications such as sepsis and organ failure. Therefore, rapid and accurate identification of the pathogen, along with targeted intervention, is of vital import...
Currently, standardized classification methods of surrounding rock are relatively insufficient. The classification of surrounding rock mainly relies on the subjective judgment of technicians, leading to diverse evaluation results. This study focuses...
Automated biological counting is essential for scaling wildlife monitoring and biodiversity assessments, as manual processing currently limits analytical effort and scalability. This review evaluates the integration of deep learning and computer visi...
Rheumatoid arthritis (RA) frequently affects the joints of the hands, with joint space narrowing (JSN) representing an important early marker of structural damage. The semi-quantitative Sharp/van der Heijde (SvdH) scoring system is widely used in cli...
Accurate and robust visual localization under changing environments remains a fundamental challenge in autonomous driving and mobile robotics. Traditional handcrafted features often degrade under long-term illumination and viewpoint variations, while...
The new European Regulation (EU) 2025/40 includes provisions on modern packaging and packaging waste. It defines the use of image QR codes on packaging (items 71 and 161) and in personal documents, making line barcodes a thing of the past. The defini...
Accurate six-degree-of-freedom (6-DoF) visual localization is a fundamental component for modern mapping and navigation. While recent data-centric approaches have leveraged Novel View Synthesis (NVS) to augment training datasets, these methods typica...
Road defect detection is essential for traffic safety and infrastructure maintenance. Excising automated methods based on 2D image analysis lack spatial context and cannot provide accurate 3D localization required for maintenance planning. We propose...
Liver fibrosis (LF) represents a crucial intermediate stage in the pathological progression from chronic liver disease to cirrhosis and hepatocellular carcinoma. Early and accurate diagnosis is of vital importance for the intervention treatment of di...
Pneumonia is one of the main reasons for child mortality, with chest radiography (CXR) being essential for its diagnosis. However, the low radiation exposure in pediatric analysis complicates the accurate detection of pneumonia, making traditional ex...
Medical image classification is a fundamental task in medical image analysis and underpins a wide range of clinical applications, including dermatological screening, retinal disease assessment, and malignant tissue detection. In recent years, diffusi...
Single-image super-resolution (SISR) is an essential low-level visual task that aims to produce high-resolution images from low-resolution inputs. However, most existing SISR methods heavily rely on ideal degradation kernels and rarely consider the a...
