Search Results (3,100)

The rapid advancement of information technology and multimedia applications has led to an increasing demand for video data processing. In particular, video fusion technology in multi-camera environments, which integrates and optimizes video data from multiple camera viewpoints, plays a crucial role in enhancing visual quality and improving the completeness of information. This technology addresses the challenge of obtaining high-quality video content in complex and dynamic environments. By improving image clarity, expanding perspective information, and enhancing scene understanding, video fusion technology has shown significant potential for a wide range of applications, attracting considerable attention from both academia and industry. Despite the existence of several review articles on video fusion, they tend to focus on isolated aspects of the technology and often lack a comprehensive, systematic overview of the field. To fill this gap, this paper provides an in-depth review of the research on video fusion technology in multi-camera scenarios. The paper covers the definition of video fusion; offers a detailed classification of key technologies, such as geometric correction and alignment, perspective fusion, spatio-temporal fusion, and multi-modal fusion; and explores its applications in diverse fields including surveillance security, virtual reality, film and television production, intelligent transportation, medical imaging, robotics, and unmanned aerial vehicles. Additionally, the paper examines the role of edge caching in video fusion, highlights the current challenges faced by the field, and discusses the potential of video fusion technology for driving innovation across multiple industries. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

Since the invention of lasers, coherent imaging has been widely employed in digital holographic microscopy. Improving the resolution of the image field remains a key challenge for achieving high-precision measurements. However, due to the high coherence of the laser, the resolution of the wavefront at the image plane depends not only on the radius of curvature of the illumination wavefront, but also on the observation position and direction. Existing theoretical approaches, which provide only approximate calculations of the amplitude distribution of the image field, are insufficient for practical applications. In this study, a theoretical framework for calculating the complex wavefield at the image plane is established, and analytical expressions describing the spectral distribution as functions of observation position and direction are derived. The proposed theory is experimentally validated using digital holographic microscopy. The results show good agreement between theory and experiment, demonstrating that the proposed approach accurately characterizes the spectral and resolution variations in the image field. These findings provide a solid theoretical foundation for the optimal design of digital holographic microscopy systems and illumination wavefields. Full article

Single-molecule Förster resonance energy transfer (smFRET) enables direct measurement of nanoscale conformational dynamics and heterogeneity in biomolecules, but quantitative interpretation of smFRET data critically depends on well-controlled excitation geometry, low background fluorescence, robust calibration, and reproducible data-analysis workflows. Prism-based total internal reflection fluorescence (pTIRF) microscopy provides important advantages for such measurements by physically separating excitation and emission paths and generating a highly confined evanescent field, yet practical guidance for implementing reproducible, quantitative pTIRF systems remains fragmented. Here we present a comprehensive, standardized framework for the design, alignment, calibration, validation, and operation of a prism-based TIRF microscope optimized for single-molecule fluorescence measurements. We describe the complete optical architecture for dual-color excitation and detection, establish alignment invariants that ensure reproducible evanescent excitation and stable donor–acceptor channel registration, and detail surface preparation, flow control, and photostabilization strategies required for reliable long-term imaging. Quantitative benchmarking protocols are introduced to evaluate signal-to-noise ratio, photobleaching kinetics, and spectral crosstalk, providing objective criteria for defining optimal operating conditions and instrument performance limits. Finally, we integrate these experimental procedures with an end-to-end single-molecule data-analysis workflow encompassing channel registration, automated and manual trajectory selection, FRET calculation, and kinetic analysis using hidden Markov modeling. The utility of the platform is demonstrated through smFRET measurements of conformational dynamics in a model nucleic acid system. Together, this work provides a reproducible and accessible methodology for implementing prism-based TIRF microscopy as a robust quantitative platform for single-molecule fluorescence studies across a wide range of biomolecular systems. Full article

Addressing the challenges of electromagnetic compatibility testing and non-destructive inspection of internal structures in miniaturized electronic devices. This paper reports a non-destructive testing method based on wide-field imaging using diamond nitrogen-vacancy (NV) centers, and systematically demonstrates its application on a black-epoxy-encapsulated Universal Serial Bus (USB) flash drive. In the experiment, a swept microwave signal from 2.82 GHz to 2.97 GHz was sequentially injected into the four external interface pins of the USB drive. A bulk diamond served as the quantum sensing layer, and optically detected magnetic resonance (ODMR) was employed to perform wide-field imaging of the microwave field distribution on the surface of the signal lines within a 1 × 1 mm² region of interest. The experimental results show that the microwave field distributions corresponding to different interface channels are significantly different. Based on these differences, the connection relationship between each signal line and its corresponding interface pin can be clearly identified, and the differences in field distribution as well as crosstalk characteristics among channels can be revealed. The method established in this work provides an effective technical pathway for non-destructive electromagnetic testing and functional verification of electronic products. Full article

This paper introduces a novel fault diagnosis framework for wiring networks that integrates Time Domain Reflectometry (TDR) with Gramian Angular Field (GAF) representations and a deep residual neural network. The proposed methodology transforms TDR responses into GAF images, which are directly exploited by the residual neural network to enable robust feature extraction from complex reflectometry signals. To support supervised learning, a forward modeling strategy is employed to generate representative TDR responses under a wide range of fault scenarios. Theframeworkis designed to provide real-time fault detection, localization, and characterization, demonstrating high effectiveness on complex topologies such as the YY-shaped network. Numerical results demonstrate high diagnostic performance for hard faults, achieving an overall accuracy and macro-averaged sensitivity exceeding 99%, thereby highlighting the effectiveness and reliability of the proposed approach. Full article

Pixelated detectors based on inorganic scintillation materials are widely used in radiation detection systems for medical imaging and many other fields of science and technology. A substantial application is X-ray scanning using flat-panel detectors (FPDs) for both fluorography and mammography. In this article, the detection properties of the monolithic planar ceramic scintillation elements are reported for the first time. A high-light yield (Gd,Y)₃Al₂Ga₃O₁₂:Ce,Mg garnet-type scintillation material was used to form square-shaped pixels, while a material of similar composition was used as a substrate. Green bodies were successfully fabricated by a digital light processing (DLP) 3D printing method. Subsequent debinding and pressureless high-temperature sintering resulted in composite elements consisting of two layers with different chemical compositions. The lower bulk layer consisted of transparent, non-luminescent garnet, whereas the upper pixelated layer, with pixel dimensions of 230 × 230 µm, was made of scintillation material. The spatial resolution of the matrices under UV light and alpha-particle excitation was evaluated. It was confirmed that the spatial resolution of the matrices produced by the developed technology is approximately 0.4 times the pixel size. The proven ability of the integrated technology of inorganic scintillation matrix production opens the way for future improvement in spatial resolution through optimizing the printed pixel dimensions. Full article

Objectives: To investigate the clinicopathologic characteristics and molecular biomarkers of atypical vasoproliferative retinal tumor (VPRT) with hemangioblastoma-like histopathologic features and concomitant von Willebrand factor (VWF) abnormalities. Methods: A 48-year-old woman undergoing phacoemulsification and 25-gauge pars plana vitrectomy with tumor resection was evaluated. Histopathological findings and immunohistochemical study of the resected tumor were performed using CD34, α-smooth muscle actin (αSMA), and glial fibrillary acidic protein (GFAP) markers. Preoperative plasma and intraoperative vitreous fluid VWF antigen levels, as well as ristocetin cofactor activity, were quantified using latex immunoturbidimetry. Results: Ultra-widefield imaging and angiography demonstrated a peripheral retinal tumor with intense vascular leakage and surrounding capillary nonperfusion. Histopathology showed hyalinized vascular components supportive of VPRT, along with abundant CD34/α-SMA-positive microvessels and scant GFAP-positive glial cells. Notably, numerous foamy vacuolated poorly differentiated cells suggested mixed hemangioblastoma-like features. Preoperative plasma VWF antigen (182.6%) and ristocetin cofactor activity (147.7%) were elevated, and vitreous VWF antigen was successfully detected at a low but distinct level (7.7%).and suggests that VWF abnormalities in the plasma and vitreous may reflect endothelial activation and/or blood–retinal barrier disruption in a subset of vascularized retinal tumors. Conclusions: Our findings demonstrate that VPRT may exhibit mixed clinicopathologic features, including hemangioblastoma-like components, which underscores the necessity of immunohistochemical assessment for definitive diagnosis. Furthermore, the quantification of VWF abnormalities in the plasma and vitreous suggests that VWF serves as a potential biomarker reflecting endothelial activation and/or blood–retinal barrier disruption in vascularized retinal tumors. Full article

Robot control based on visual information perception has been a hot topic in the field of industrial robots, and the use of visual servoing technology to guide robots for high-precision spatial localization of machined workpieces has a wide range of application value. Aiming at the camera hand–eye calibration error and robot repositioning error, which have a large impact on the spatial localization and navigation accuracy, and when the binocular camera Z-direction accuracy is not high enough and the viewing angle is limited, etc., we propose a spatial visual servoing algorithm based on an orthogonal vision system that combines an eye-in-hand camera and an eye-to-hand camera in a hybrid configuration. By extracting sub-pixel image features in real time and deriving directionally decoupled interaction matrices, a linear controller is designed to guide the robot in the XY-plane and Z-direction separately. This decoupling strategy enlarges the convergence domain, avoids local minima caused by coupled degrees of freedom, and enhances system stability. To this end, the intrinsic calibration and hand–eye calibration of two cameras placed orthogonally are carried out firstly, and the accuracy of hand–eye calibration is not too demanding; then the sub-pixel level image position of the target is extracted in real time and the interaction matrix is derived and a linear controller is designed to control the robot’s motion; finally, the experiments of spatial localization accuracy are completed on the KUKA iiwa to validate the effectiveness of the method. Full article

The solution theory of Sylvester-type equations finds wide applications in control theory, robotics, and image processing. This paper systematically surveys, classifies and summarizes the existing research results of three classes of Sylvester-type equations: matrix equations, tensor equations, and operator equations. It extracts nine mainstream research methods and clarifies the internal correlations among these methods, as well as their applicable equation types. Combined with four prior review articles focusing on special cases of Sylvester-type equations, this work establishes a comprehensive framework for solving such equations. It not only provides a systematic theoretical foundation and a clear research thread for subsequent researchers but also offers valuable methodological insights for further investigations in related fields. Full article

The high-precision celestial positioning of space targets is constrained by star point centroid errors, star identification errors, and residual distortions in wide-field imaging. To improve the positioning accuracy and robustness under complex stellar-field conditions, this study focuses on improving star point centroid extraction and star identification. For star point centroid extraction, an improved effective point spread function (ePSF) fitting method is adopted to construct an ePSF model consistent with the actual imaging process, which characterizes the instrumental response, pixel sampling, and stellar intensity distribution, thereby improving the accuracy of sub-pixel centroid extraction. For star identification, a two-level matching method combining the inradius of star triangles and angular-distance constraints is proposed. Candidate screening, angular-distance constraints, and posterior validation based on a theoretical reference star map are used to reduce redundant matches and mismatching risks. Experiments on simulated star images show that the star identification success rate of the proposed method reaches 97.32%, outperforming traditional algorithms. In real star images, the star identification precision, star identification completeness, and F₁ score are 93.59%, 90.14%, and 91.83%, respectively. When the 20-constant plate model is adopted, the average positioning errors of simulated and real star images are reduced to 0.86″ and 1.10″, respectively. Further increasing the model to 30 constants provides limited accuracy gain, which is insufficient to fully offset the cost of increased model complexity and parameter stability. The results show that the proposed method achieves a favorable balance among positioning accuracy, identification reliability, and model complexity. Full article

Oil and gas pipelines are crucial component of the strategic infrastructure in China, but they are severely threatened by geological disasters in complex terrains. These disasters may cause pipeline rupture, leakage or explosion, resulting in significant economic losses, environmental pollution and casualties. Traditional manual disaster investigation is inefficient because the pipelines are widely distributed, access is limited and the terrain may be rugged. Therefore, efficient and accurate disaster identification and risk assessment have become a priority that the industry urgently needs to address. Taking the Jiangxi section of the West Line II Zhangshu–Xiangtan connection line as the research area, this study combines the SBAS-InSAR technology with spatial analysis based on GIS to support early disaster identification, surface deformation monitoring and vulnerability assessment. The analysis of 48 Sentinel-1A satellite images shows that the regional ground deformation range is −19.5 to 19.1 mm per year, and most areas show a slow deformation of within ±10 mm per year. The preliminary visual interpretation of the SBAS-InSAR ground deformation data yields 121 preliminary high-deformation disaster points. Combined with the 9 key assessment factors in the GIS platform and the entropy-weighted information model obtained from the geological disaster susceptibility evaluation map and using the optical remote sensing images, 21 human interference points are excluded, and finally 100 potential geological disaster hazard areas are retained. Field verification was conducted through ground reconnaissance surveys and confirmed that 78 of these areas have geological disaster hazards such as landslide, collapses, and slope water damage, providing solid technical support for geological disaster management, monitoring and early warning along the pipeline route. This study proposes a multi-source integrated framework combining SBAS-InSAR, GIS-based susceptibility assessment, and optical validation for improving the reliability of early geological hazard identification. Full article

Paper-based logistics documents remain widely used in multi-enterprise supply chains, where heterogeneous layouts, noisy document images, non-standard material descriptions, and limited edge-computing resources make structured extraction and material matching difficult. This paper proposes RRA-Logis, a lightweight multimodal large-language-model framework for logistics document understanding and material entity alignment. Instead of treating logistics document processing as a conventional field-extraction task, RRA-Logis formulates it as a document-to-entity alignment problem under resource constraints. The framework combines schema-constrained image-to-JSON extraction, LoRA/QLoRA instruction tuning, vector-based candidate recall, LLM-based semantic verification, confidence-gated decision making, and human-in-the-loop data evolution. Its methodological contribution lies in organizing these components into a resource-aware decision mechanism that determines whether a material-matching result should be automatically accepted or routed to human verification according to confidence and ambiguity margins. Experiments under a 24 GB VRAM constraint show that the fine-tuned Qwen2.5-VL-7B model achieves 85.4% document-level extraction accuracy and 100% JSON compliance on L-Doc-2K, while the proposed two-stage material-alignment method achieves 92.8% Top-1 accuracy. Ablation results indicate that LLM-based re-ranking, fused scoring, and confidence-gated verification each contribute to improved alignment reliability. Additional evaluation on the public DocILE benchmark and a desensitized real-document subset further examines cross-domain extraction transfer and the gap between Sim-to-Real data and operational logistics documents. The results suggest that RRA-Logis provides a practical framework for logistics document automation under constrained computing resources, while larger-scale real-world validation and broader benchmarking against specialized document-intelligence systems remain necessary. Full article

In recent years, the YOLO series has emerged as a widely adopted framework for real-time object detection because of its favorable balance between detection accuracy and inference efficiency. Nevertheless, accurate recognition and localization of tiny defects in industrial inspection remain challenging. These challenges mainly arise from the extremely small scale of defect targets, low image contrast, and the limited capability of conventional models in feature representation under uniform backgrounds. To address these issues from a mathematically optimized perspective and via feature modeling optimization, we develop a dedicated framework for tiny defect detection, termed SLA-YOLO. The main contributions of this work are as follows. First, we adopt a slicing-based processing strategy inspired by the SAHI framework, referred to as Image Slicing Processing (ISP) in this work, and extend it to both training and inference stages. This design enhances the relative scale of tiny defects within local regions, improving detection sensitivity and data diversity without introducing additional model complexity. Second, we introduce a Large Receptive-Field Selective Context (LRSC) module. By leveraging large-receptive-field selective convolution kernels, this module adaptively captures contextual information around critical defect regions via feature modeling optimization of scale-dependent representations. Third, we incorporate a Transformer-based High-level Feature Enhancement (THFE) module to improve global dependency modeling in high-level semantic representations, thereby enhancing feature discriminability for complex defect patterns. Experimental results on the CCB defect dataset show that SLA-YOLO improves mAP@50:95 by 2.7% and mAP@50 by 3.3%. In addition, the proposed method demonstrates strong generalization capability on other tiny object detection tasks. Full article

The solar corona has an extremely low density, and its brightness is only about one millionth of that of the photosphere. High-dynamic-range imaging of its faint structure is therefore essential for studying coronal heating, coronal mass ejections, and space weather. Quantitative coronagraph imaging requires flat-field measurement and calibration, which underpin intensity calibration, small-scale feature detection, and long-term cyclic analysis. This paper analyzes the coronagraph imaging chain (baffle–optical system–detector) and the origins of flat-field errors, including optical aberrations, stray light, and pixel-response non-uniformity, and summarizes the resulting calibration requirements of next-generation coronagraphs. On this basis, ground-based and space-based flat-fielding methods are systematically reviewed: the ground-based methods include integrating-sphere uniform light sources, opal glass/diffuser plates, clear-sky and thin-cloud backgrounds, and solar disk scanning, while the space-based methods include internal light sources and diffuser plates, attitude-roll and off-corona offset observations, and multi-phase statistical self-consistent flat-fielding. Their accuracy, resource cost, and applicability are compared. The review shows that no single method is simultaneously high-precision, easy to update, and engineer-friendly; a hierarchical, multi-method calibration framework is therefore recommended. Finally, a new method is proposed in which lithographically generated structured light fields, combined with Fourier optics and machine learning inversion, are used to estimate the pixel-response function. Preliminary experiments show that this method achieves a lower residual error than the integrating-sphere and opal glass methods, providing a high-precision reference for future wide-band, high-resolution coronagraph calibration. Full article