Search Results (1,457)

Over the course of NASA’s Dawn Discovery mission, the onboard framing camera mapped Ceres across a wide wavelength spectrum at varying polar science orbits and altitudes. With increasing resolution, the uniqueness of the 92 km wide, young Occator crater became evident. Its central cryovolcanic dome, Cerealia Tholus, and especially the associated bright carbonate and ammonium chloride deposits—named Cerealia Facula and the thinner, more dispersed Vinalia Faculae—are the surface expressions of a deep brine reservoir beneath Occator. Understandably, this made this crater the target for future sample return mission studies. The planning and preparation for this kind of mission require the characterization of potential landing sites based on the most accurate topography and orthorectified image data. In this work, we demonstrate the capabilities of the freely available and open-source USGS Integrated Software for Imagers and Spectrometers (ISIS 3) and Ames Stereo Pipeline (ASP 2.7) in creating high-quality image data products as well as stereophotogrammetric (SPG) and multi-view shape-from-shading (SfS) digital terrain models (DTMs) of the aforementioned spectroscopically challenging features. The main data products of our work are four new DTMs, including one SPG and one SfS DTM based on High-Altitude Mapping Orbit (HAMO) (CSH/CXJ) and one SPG and one SfS DTM based on Low-Altitude Mapping Orbit (LAMO) (CSL/CXL), along with selected Extended Mission Orbit 7 (XMO7) framing camera (FC) data. The SPG and SfS DTMs were calculated to a GSD of 1 and 0.5 px, corresponding to 136 m (HAMO SPG), 68 m (HAMO SfS), 34 m (LAMO SPG), and 17 m (LAMO SfS). Finally, we show that the SPG and SfS approaches we used yield consistent results even in the presence of high albedo differences and highlight how our new DTMs differ from those previously created and published by the German Aerospace Center (DLR) and the Jet Propulsion Laboratory (JPL). Full article

In recent years, Vision-Based Navigation (VBN) techniques have emerged as a fundamental component to enable autonomous spacecraft operations, particularly in challenging environments such as planetary landings, where ground control may be limited or unavailable. Developing and testing VBN algorithms requires the availability of a large number of realistic images of the application scenario; however, these are rarely available. This paper presents a novel rendering software tool to generate accurate synthetic optical images of the lunar surface by leveraging high-resolution Digital Terrain Models (DTMs). Unlike traditional ray-tracing algorithms, the method iteratively propagates camera rays to determine their intersection with the terrain surface defined by a Digital Elevation Model (DEM). The color information is then retrieved from the corresponding Digital Orthophoto Model (DOM) through the knowledge of the ray impact points, bypassing the need for the costly computation of shadows, reflections, and refractions effects. The rendering performance is demonstrated through a comprehensive selection of images of the lunar surface under different illumination conditions and camera orientations. Full article

Currently, various deep learning methods can assist in medical diagnosis. Coronary Digital Subtraction Angiography (DSA) is a medical imaging technology used in cardiac interventional procedures. By employing X-ray sensors to visualize the coronary arteries, it generates two-dimensional images from any angle. However, due to the complexity of the coronary structures, the 2D images may sometimes lack sufficient information, necessitating the construction of a 3D model. Camera-level 3D modeling can be realized based on deep learning. Nevertheless, the beating of the heart results in varying degrees of arterial vasoconstriction and vasodilation, leading to substantial discrepancies between DSA sequences, which introduce errors in 3D modeling of the coronary arteries, resulting in the inability of the 3D model to reflect the coronary arteries. We propose a coronary DSA video sequence keyframe recognition method, CDKD-w+, based on w+ space encoding. The method utilizes a pSp encoder to encode the coronary DSA images, converting them into latent codes in the w+ space. Differential analysis of inter-frame latent codes is employed for heartbeat keyframe localization, aiding in coronary 3D modeling. Experimental results on a self-constructed coronary DSA heartbeat keyframe recognition dataset demonstrate an accuracy of 97%, outperforming traditional metrics such as L1, SSIM, and PSNR. Full article

The purpose of the present study was to evaluate mast cell density in squamous cell carcinoma tissues of dogs and cats to assess species differences. Skin squamous cell carcinoma tissues from dogs (n = 15: n = 10 from body sites and n = 5 nail bed specimens) and cats (n = 15, n = 10 from ears and n = 5 nasal planum specimens) were examined. Intratumoral mast cell density (IMCD), peritumoral mast cell density (PMCD) and total mast cells density (TMCD) as a sum of IMCD and PMCD were calculated from Giemsa-stained slides at high magnification in 1 mm² using an Olympus microscope (Olympus BX41, Tokyo, Japan) equipped with a digital Olympus DP72 image camera and CellSensDimension software V1.16). Both intratumoral and peritumoral tissues of the squa.mous cell carcinoma were divided into two categories: (1) loose, well-vascularized, rich in lymphocytes and plasmocytes, macrophages and neutrophils; and (2) fibrous, with few or no lymphocytes, plasmocytes, macrophages and neutrophils (the presence of neutrophils can be associated with actinic keratosis, mechanical irritation of the tumor in some anatomical areas during scratching with teeth, but, in general, neutrophils are associated with more invasive squamous cell carcinoma). In cats, a markedly higher total number of mast cells was found, and the number was also higher in intratumoral and peritumoral tissues. A similar tendency was found in both dogs and cats—a markedly higher number of mastocytes was found in both peritumoral and intratumoral loose, well-vascularized connective tissue. Conversely, lower numbers of mast cells were found in both intratumoral and peritumoral compact fibrous tissue in both animal species. Full article

Floc size distribution and settling velocities are crucial parameters for characterising cohesive sediments, as they influence how these sediments behave in various environmental settings. The accurate measurement of these properties is essential, with different methods available depending on the scope of the study. For long-term monitoring, in situ techniques based on laser diffraction are commonly used, while video microscopy techniques are preferred for shorter studies due to their ability to provide detailed information on individual particles. This study compares two high-magnification digital video camera setups, LabSFLOC-2 and FLOCCAM, to investigate the impact of particle concentration on settling velocity in flocculated sediments. Flocculated clay was introduced into settling columns, where both the size and settling velocities of the flocs were measured. The results obtained from both setups are in line with each other, even though the FLOCCAM was slightly more efficient at capturing images of small particles (of size less than 50 microns) and LabsFLOC-2 was better at detecting large size fraction particles (having a low contrast due to the presence of organic matter). Floc size and settling velocity measurements from both setups however exhibit mostly similar trends as a function of clay concentration and the same order of magnitudes for the recorded settling velocities. Full article

Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing. Full article

Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric parameters, reprojection error, projection accuracy, angles of intersection, distance, and the number of cameras used in tie point calculations. The approach is validated using point cloud data from the Temple of Neptune in Paestum, Italy. The results show that the proposed method significantly improves 3D reconstruction quality, with k-means outperforming other clustering techniques based on three evaluation metrics. This method offers superior versatility and performance compared to traditional and machine learning techniques, demonstrating its potential to enhance UAV-based surveying and inspection practices. Full article

With the acceleration of global aging, outdoor environments, especially urban green space’s planning and design, play a crucial role in not only promoting physical health but also significantly increasing the opportunities for social interactions for older adults. In recent years, the study of age-friendly outdoor environments has attracted increasing attention, with digital methods emerging as essential tools due to their precision and versatility. In this research, three parks in the Nankai District, Tianjin, are taken as the subject of a case study to explore the spatial factors that may exert influence on the behavior distribution of older adults in summery urban parks’ planning and design. With the behavior data of the older adults in the park collected using an Insta360 camera every hour (from 8 a.m. to 15 p.m.), the three parks are divided into a total of 49 areas for further analysis. Additionally, the visual indexes of the spatial syntax are analyzed with Depthmap 10, the sunlight conditions are analyzed with the Tangent model, and some other spatial factors, such as the green space ratio and the hard ground ratio, are calculated according to the semantic segmentation of the 360-degree panoramic view photo from the center of every area. SPSS and Gradient Boosting Decision Trees (GBDTs) are used to reveal not only the correlations between the sunlight conditions and the behavior distribution of behavior of the older adults, but also the importance ranking of spatial factors. Furthermore, some improvement strategies are proposed for spatial facility configuration, park furniture arrangement, rational hardscape planning, as well as greening and landscape design. By exploring how to improve the spatial planning and design of summery urban green space for older adults, this research provides guidance on the creation of urban green spaces in extremely hot weather that are not only visually appealing but also socially equitable and environmentally sustainable. Full article

This paper considers the features of a time series of plankton concentrations, which are further compared with such phenomena as the alteration of day and night and tidal processes. The analysis of experimental data recorded as a result of long-term monitoring measurements under field conditions showed that the diurnal variability in plankton concentrations can be described using a model harmonic function. At the same time, based on the parameters of the diurnal variability model, it is possible to build a bioindication system to detect the influence of abnormal environmental factors estimated as pollution. This study discusses the ideology of building such a system based on regular observations of the behavior of autochthonous plankton using a submersible digital holographic camera. Full article

The dynamic identification of the deformation of a noise barrier column is of great significance to the monitoring of its health. At the same time, the maximum stress of the column is an important indicator for the evaluation of its health status. Traditional contact displacement monitoring installs sensors on the structure, requires a lot of wiring and data acquisition equipment, and establishes a relatively independent and stable displacement reference system. Affected by the environment, wear, and material aging, the efficiency and reliability of data acquisition are reduced. A monitoring method based on digital image has the advantages of non-contact monitoring, high precision, and strong reliability. The existing DIC detection methods are limited by processor performance and image resolution, which are difficult to apply to engineering detection. In this paper, a structural displacement identification method based on convolutional neural networks (CNNs) and DIC technology is proposed. In this method, the data set is formed according to the column displacement cloud image obtained by DIC analysis, and the data set is enhanced by data normalization and region division. Through the analysis of the number of network layers and learning rate, the model design of the deep learning network is carried out. The high-speed camera image results of the test are introduced and identified by the static loading test of the equal-scale sound barrier. The results show that the structural displacement identification method based on CNN and DIC technology can accurately identify the displacement change in the structure, which greatly improves the efficiency of image displacement calculation using DIC technology. Full article

Background/Objectives: Performing hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) on the same specimen slide provides advantages that include specimen conservation and the ability to combine the H&E context with biomarker expression at the individual cell level. We previously used invisible deposited chromogens and dual-camera imaging, including monochrome and color cameras, to implement simultaneous H&E and IHC. Using this approach, conventional H&E staining could be simultaneously viewed in color on a computer monitor alongside a monochrome video of the invisible IHC staining, while manually scanning the specimen. Methods: We have now simplified the microscope system to a single camera and increased the IHC multiplexing to four biomarkers using translational assays. The color camera used in this approach also enabled multispectral imaging, similar to monochrome cameras. Results: Application is made to several clinically relevant specimens, including breast cancer (HER2, ER, and PR), prostate cancer (PSMA, P504S, basal cell, and CD8), Hodgkin’s lymphoma (CD15 and CD30), and melanoma (LAG3). Additionally, invisible chromogenic IHC was combined with conventional DAB IHC to present a multiplex IHC assay with unobscured DAB staining, suitable for visual interrogation. Conclusions: Simultaneous staining and detection, as described here, provides the pathologist a means to evaluate complex multiplexed assays, while seated at the microscope, with the added multispectral imaging capability to support digital pathology and artificial intelligence workflows of the future. Full article

Timely approvals and payments to the project participants are crucial for successful completion of construction projects. However, the construction industry faces persistent delays and non-payments to contractors. Despite the desirable benefits of automated payments and enhanced access to digitized data progress, most payment applications rely on centralized control mechanisms; inefficient procedures; and documentation that takes time to prepare, review, and approve. As such, there is a need for a reliable payment automation system that guarantees timely execution of payments upon the detection of completed works. Therefore, this study used a cutting-edge approach to automate construction payments by integrating blockchain-enabled smart contracts and scan-to-Building Information Modeling (BIM). In this approach, scan-to-BIM provides accurate, real-time building progress data, which serve as the source of verifiable off-chain data. A chain-link is then used to securely relay these data to the blockchain system. Blockchain-enabled smart contracts automate the execution of payments upon meeting contract conditions. The proposed approach was implemented on a real case study project. The actual site scan was captured using a photogrammetry 360° camera, which uses a combination of structured light and infrared depth sensing technology to capture 3D data and create detailed 3D models of spaces. This study leveraged accurate, real-time building progress data to automate payments using blockchain-enabled smart contracts upon work completion, thus reducing payment disputes by tying payments to verifiable construction progress, leading to faster release of payments. The findings show that this approach provides a transparent basis for payment, enhancing trust and allowing precise project progress tracking. Full article

Background/Objectives: Cardiovascular diseases are a leading cause of death, and psychosocial stress is considered a contributing factor to these issues. With the rising number of heart surgeries, proper rehabilitation post-surgery is essential. Previous studies have demonstrated the positive impact of yoga and transcendental meditation on the cardiovascular system. This pilot study aimed to investigate the effects of yoga and transcendental meditation on retinal microcirculation in cardiac patients before (admission), after (discharge), and following (3 weeks after discharge) rehabilitation. Methods: This study examined changes in retinal microcirculation in three rehabilitation groups of patients after heart surgery. The control group received standard exercise therapy, while the meditation group incorporated 20 min of meditation, and the yoga group incorporated 20 min of yoga practice, twice per day for the duration of four weeks of rehabilitation. Retinal images were captured using a non-mydriatic digital retinal camera (Canon CR-2, Canon Medical Systems Europe B.V., Netherlands), and the microcirculation parameters central retinal artery equivalent, central retinal vein equivalent, and artery-to-vein ratio were analyzed using MONA REVA software ((version 2.1.1), VITO, Mol, Belgium). Repeated measures ANOVA was performed to evaluate differences between the three groups in the course of rehabilitation. Results: None of the parameters revealed significant differences in retinal microcirculation between the three rehabilitation groups. Conclusions: The study evaluating changes in retinal microcirculation, as an indicator of central circulation in cardiac patients undergoing rehabilitation, did not observe any significant changes. As yoga and meditation are underestimated approaches in cardiac rehabilitation, this pilot study acts as a basis for providing preliminary information for future studies to encourage the research community to fill the gap in this area. Full article

The photo-response non-uniformity (PRNU) noise of imaging sensors significantly aids digital forensics and judicial identification, as it can be used as the fingerprint for uniquely identifying individual imaging devices. During the PRNU fingerprint extraction, it is very important for source camera identification to estimate the natural noise from real-world images. Most existing deep learning-based neural networks have a large number of model parameters, and they may not be practical in realistic scenarios such as deploying the model on small devices like smartphones and remote forensics equipment. In this paper, we present a lightweight PRNU fingerprint extraction algorithm based on an invertible denoising network (InvDN) for source camera identification. Specifically, to reduce the number of parameters, the deep network uses a constant amount of memory to compute the gradient and employs the same parameters for both forward and backward propagation. In addition, by introducing an information-loss-less reversible network, more complete PRNU fingerprint information can be extracted. Experimental results show that this algorithm exhibits superior PRNU fingerprint extraction performance and generalization ability compared to the state-of-the-art PRNU fingerprint extraction algorithms. Full article

Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search algorithm is crucial for the effective implementation of camera source identification. Despite its importance, this domain has witnessed limited research, with existing studies predominantly focusing on search efficiency while neglecting robustness, which is essential. In practical scenarios, query images often suffer from poor signal quality due to noise, and the variability in fingerprint quality across different sources presents a significant challenge. Conventional brute-force search algorithms (BFSAs) prove largely ineffective under these conditions because they lack the necessary robustness. This paper addresses the issues in digital image source identification by proposing a rapid fingerprint search algorithm based on global information. The algorithm innovatively introduces a search priority queue (SPQ), which analyzes the global correlation between the query fingerprint and all reference fingerprints in the database to construct a comprehensive priority ranking, thereby achieving the efficient retrieval of matching fingerprints. Compared to the traditional brute-force search algorithm (BFSA), our method significantly reduces computational complexity in large-scale databases, optimizing from $O\left(nN\right)$ to $O(nlogN)$, where n is the length of the fingerprint, and N is the number of fingerprints in the database. Additionally, the algorithm demonstrates strong robustness to noise, maintaining a high matching accuracy rate even when image quality is poor and noise interference is significant. Experimental results show that in a database containing fingerprints from 70 cameras, our algorithm is 50% faster in average search time than BFSA, and its matching accuracy rate exceeds 90% under various noise levels. This method not only improves the efficiency and accuracy of digital image source identification but also provides strong technical support for handling large-scale image data, with broad application prospects in fields such as copyright protection and forensic evidence. Full article