Hasil untuk "Photography"

Anahita Mobaseri, Leonardo Gordillo, Charles Burton et al.

Combining high-speed photography with direct force measurements, we investigate the impact dynamics of drops of cornstarch-water mixtures -- a premier example of shear-thickening fluids -- across a wide range of impact conditions. Our study identifies three distinct impact regimes. In addition to the liquid-like and solid-like behaviors generally expected for the impact-induced response of shear-thickening fluids, we uncover a counterintuitive regime in which high-concentration cornstarch-water mixtures display a liquid-like response at the onset of impact when shear rates are high and only transition to a solid-like behavior at later times as shear rates reduce. By integrating the classic drop-impact theory with the Reynolds-Darcy mechanism for dilatancy, we develop a unified model that quantitatively describes the impact dynamics of shear-thickening drops across all regimes. Our work reveals the unexpected response of shear-thickening fluids to ultra-fast deformation and advances fundamental understanding of drop impact for complex fluids.

Yongjian Li, Song Ji, Danchao Gong et al.

The development of satellite remote sensing technology has made it easier to obtain satellite imagery. Compared to imagery obtained from aerial photography, satellite imagery has the advantages of wide coverage, high acquisition efficiency, and periodic revisits. In photogrammetry, the 3-D reconstruction technology of satellite imagery often requires optimization and adjustment of numerous rational polynomial coefficient parameters, which to some extent limits the speed and accuracy of 3-D reconstruction. At the same time, the progress in 3-D reconstruction technology in the field of computer vision has shown certain advantages in terms of accuracy and speed. However, these methods are specifically designed for pinhole imaging models and cannot be directly applied to the 3-D reconstruction of satellite imagery with row-sampled central projection. The introduction of the equivalent pinhole imaging model enables computer vision methods to perform 3-D reconstruction on satellite imagery. This local approximation introduces reprojection errors when the RPC model is equivalent to the pinhole imaging model, thereby affecting the accuracy of 3-D reconstruction. This article investigates the causes and patterns of reprojection errors in the equivalent pinhole imaging model and proposes a method for generating pseudoimages through iterative resampling, as well as a method for partitioning satellite images to equivalently approximate the pinhole imaging model. Test results on the MVS3D dataset show that both methods can reduce reprojection errors, thereby improving the accuracy of 3-D reconstruction of satellite images using the equivalent pinhole imaging model.

Zhargalma Alymbaeva, Margarita Zharnikova, Alexander Ayurzhanaev et al.

This paper presents an assessment of potential losses and damage costs to rare coastal plant species of Lake Baikal (UNESCO World Heritage Site) as a result of inundation at high water levels. The lake’s ecosystem is characterized by an exceptional diversity of rare and endemic animal and plant species. The construction of a hydroelectric power plant caused an increase in the water level of Lake Baikal, resulting in the inundation of low-lying coastal areas, the destruction of the coastline, alterations to the hydrological regime, etc. However, there are practically no works devoted to water-level modeling and the assessment of its impact on riparian vegetation, including rare species. We conducted fieldwork to determine the abundance of four vulnerable species and identified inundation zones at different high water levels on the basis of digital elevation models based on aerial photography data. The analysis revealed that at the maximum level of inundation, the number of plant species affected would total 5164, amounting to a financial loss of biodiversity estimated at 3098.4 thousand rubles. To mitigate the projected losses, it is imperative to implement measures that restrict water-level fluctuations above the 457.00 m threshold. The absence of flora as an object of state environmental monitoring, which is not specified in the regulatory legal document, must be rectified in a timely manner.

Clemens Seibold, Clemens Seibold, Eric L. Wisotzky et al.

IntroductionDeepfakes have become ubiquitous in our modern society, with both their quantity and quality increasing. The current evolution of image generation techniques makes the detection of manipulated content through visual inspection increasingly difficult. This challenge has motivated researchers to analyze heart-beat-related signal to distinguish deep fakes from genuine videos.MethodsIn this study, we analyze deepfake videos of faces generated with novel methods regarding their heart-beat-related signals using remote photoplethysmography (rPPG). The rPPG signal describes the blood flow based, or rather local blood volume changes, and thus reflects the pulse signal. For our analysis, we present a pipeline that extracts rPPG signals and investigate the origin of the extracted signals in deepfake videos using correlation analyses. To validate our rPPG extraction pipeline and analyze rPPG signals of deepfakes, we captured a dataset of facial videos synchronized with an electrocardiogram (ECG) as a ground-truth pulse signal. Additionally, we generated high-quality deepfakes and incorporated publicly available datasets into our evaluation.ResultsWe prove that our heart rate extraction pipeline produces valid estimates for genuine videos by comparing the estimated results with ECG reference data. Our high-quality deepfakes exhibit valid heart rates and their rPPG signals show a significant correlation with the corresponding driver video that was used to generate them. Furthermore, we show that this also holds for deepfakes from a publicly available dataset.DiscussionPrevious research assumed that the subtle heart-beat-related signals get lost during the deepfake generation process, making them useful for deepfake detection. However, this paper shows that this assumption is no longer valid for current deepfake methods. Nevertheless, preliminary experiments indicate that analyzing spatial distribution of bloodflow regarding its plausibility can still help to detect high quality deepfakes.

Wenyi Hu, Zhihong Lin, Malcolm Clark et al.

Abstract We aim to assess the real-world accuracy (primary outcome), feasibility and acceptability (secondary outcomes) of an automated retinal photography and artificial intelligence (AI)-based cardiovascular disease (CVD) risk assessment system (rpCVD) in Australian primary care settings. Participants aged 45–70 years who had recently undergone all or part of a CVD risk assessment were recruited from two general practice clinics in Victoria, Australia. After consenting, participants underwent retinal imaging using an automated fundus camera, and an rpCVD risk score was generated by a deep learning algorithm. This score was compared against the World Health Organisation (WHO) CVD risk score, which incorporates age, sex, and other clinical risk factors. The predictive accuracy of the rpCVD and WHO CVD risk scores for 10-year incident CVD events was evaluated using data from the UK Biobank, with the accuracy of each system assessed through the area under the receiver operating characteristic curve (AUC). Participant satisfaction was assessed through a survey, and the imaging success rate was determined by the percentage of individuals with images of sufficient quality to produce an rpCVD risk score. Of the 361 participants, 339 received an rpCVD risk score, resulting in a 93.9% imaging success rate. The rpCVD risk scores showed a moderate correlation with the WHO CVD risk scores (Pearson correlation coefficient [PCC] = 0.526, 95% CI: 0.444–0.599). Despite this, the rpCVD system, which relies solely on retinal images, demonstrated a similar level of accuracy in predicting 10-year incident CVD (AUC = 0.672, 95% CI: 0.658-0.686) compared to the WHO CVD risk score (AUC = 0.693, 95% CI: 0.680-0.707). High satisfaction rates were reported, with 92.5% of participants and 87.5% of general practitioners (GPs) expressing satisfaction with the system. The automated rpCVD system, using only retinal photographs, demonstrated predictive accuracy comparable to the WHO CVD risk score, which incorporates multiple clinical factors including age, the most heavily weighted factor for CVD prediction. This underscores the potential of the rpCVD approach as a faster, easier, and non-invasive alternative for CVD risk assessment in primary care settings, avoiding the need for more complex clinical procedures.

Shuwei Guo, Simin Luan, Yan Ke et al.

Moiré patterns arise from spectral aliasing between display pixel lattices and camera sensor grids, manifesting as anisotropic, multi-scale artifacts that pose significant challenges for digital image demoiréing. We propose MoiréNet, a convolutional neural U-Net-based framework that synergistically integrates frequency and spatial domain features for effective artifact removal. MoiréNet introduces two key components: a Directional Frequency-Spatial Encoder (DFSE) that discerns moiré orientation via directional difference convolution, and a Frequency-Spatial Adaptive Selector (FSAS) that enables precise, feature-adaptive suppression. Extensive experiments demonstrate that MoiréNet achieves state-of-the-art performance on public and actively used datasets while being highly parameter-efficient. With only 5.513M parameters, representing a 48% reduction compared to ESDNet-L, MoiréNet combines superior restoration quality with parameter efficiency, making it well-suited for resource-constrained applications including smartphone photography, industrial imaging, and augmented reality.

Xuanzhao Dong, Wenhui Zhu, Yujian Xiong et al.

Color fundus photography (CFP) is central to diagnosing and monitoring retinal disease, yet its acquisition variability (e.g., illumination changes) often degrades image quality, which motivates robust enhancement methods. Unpaired enhancement pipelines are typically GAN-based, however, they can distort clinically critical vasculature, altering vessel topology and endpoint integrity. Motivated by these structural alterations, we propose Vessel-Aware Optimal Transport (\textbf{VAOT}), a framework that combines an optimal-transport objective with two structure-preserving regularizers: (i) a skeleton-based loss to maintain global vascular connectivity and (ii) an endpoint-aware loss to stabilize local termini. These constraints guide learning in the unpaired setting, reducing noise while preserving vessel structure. Experimental results on synthetic degradation benchmark and downstream evaluations in vessel and lesion segmentation demonstrate the superiority of the proposed methods against several state-of-the art baselines. The code is available at https://github.com/Retinal-Research/VAOT

Nassira DJEGHRI ZAROUTA

Abstract: The study raises the issue of documenting news sites for media bias through photographs that embody the Al-Aqsa flood that struck and deepened the moral and material tragedies and wounds of the Palestinians due to the occupier who disregarded all humanitarian treaties and conventions. It became clear to us after thorough analysis that the levels of this bias vary, shifting between supports and fine-tuning in visions between the East and the West. The results also confirmed that media images could be decisive in conveying geopolitical trends, conflicts, and global blocs. Undoubtedly, the current study is trying hard to stand on the representations of bias in the dealing.  Keywords: Al-Aqsa flood; news sites; media bias; photograph; geopolitical discourse

Jorge Yero Salazar, Pablo Rivas, Renato Borras-Chavez et al.

This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga leptonyx}, a key species within Antarctic ecosystems, to review the different available methods found. As apex predators, Leopard seals are characterized by their significant ecological role and elusive nature so studying them is crucial to understand the health of their ecosystem. Traditional methods of monitoring seal species are often constrained by the labor-intensive and time-consuming processes required for collecting data, compounded by the limited insights these methods provide. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring. By leveraging state-of-the-art approaches in detection, segmentation, and recognition within digital imaging, this paper presents a synthesis of the current landscape, highlighting both the cutting-edge methodologies and the predominant challenges faced in accurately identifying seals through photographic data.

Aryan Garg, Raghav Mallampali, Akshat Joshi et al.

Dual pixels contain disparity cues arising from the defocus blur. This disparity information is useful for many vision tasks ranging from autonomous driving to 3D creative realism. However, directly estimating disparity from dual pixels is less accurate. This work hypothesizes that distilling high-precision dark stereo knowledge, implicitly or explicitly, to efficient dual-pixel student networks enables faithful reconstructions. This dark knowledge distillation should also alleviate stereo-synchronization setup and calibration costs while dramatically increasing parameter and inference time efficiency. We collect the first and largest 3-view dual-pixel video dataset, dpMV, to validate our explicit dark knowledge distillation hypothesis. We show that these methods outperform purely monocular solutions, especially in challenging foreground-background separation regions using faithful guidance from dual pixels. Finally, we demonstrate an unconventional use case unlocked by dpMV and implicit dark knowledge distillation from an ensemble of teachers for Light Field (LF) video reconstruction. Our LF video reconstruction method is the fastest and most temporally consistent to date. It remains competitive in reconstruction fidelity while offering many other essential properties like high parameter efficiency, implicit disocclusion handling, zero-shot cross-dataset transfer, geometrically consistent inference on higher spatial-angular resolutions, and adaptive baseline control. All source code is available at the anonymous repository https://github.com/Aryan-Garg.

Armen V. Gaboutchian, Vladimir A. Knyaz, Evgeniy N. Maschenko et al.

The interest in the development of dental enamel thickness measurement techniques is connected to the importance of metric data in taxonomic assessments and evolutionary research as well as in other directions of dental studies. At the same time, advances in non-destructive imaging techniques and the application of scanning methods, such as micro-focus-computed X-ray tomography, has enabled researchers to study the internal morpho-histological layers of teeth with a greater degree of accuracy and detail. These tendencies have contributed to changes in established views in different areas of dental research, ranging from the interpretation of morphology to metric assessments. In fact, a significant amount of data have been obtained using traditional metric techniques, which now should be critically reassessed using current technologies and methodologies. Hence, we propose new approaches for measuring dental enamel thickness using palaeontological material from the territories of northern Vietnam by means of automated and manually operated techniques. We also discuss method improvements, taking into account their relevance for dental morphology and occlusion. As we have shown, our approaches demonstrate the potential to form closer links between the metric data and dental morphology and provide the possibility for objective and replicable studies on dental enamel thickness through the application of automated techniques. These features are likely to be effective in more profound taxonomic research and for the development of metric and analytical systems. Our technique provides scope for its targeted application in clinical methods, which could help to reveal functional changes in the masticatory system. However, this will likely require improvements in clinically applicable imaging techniques.

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Steven Tel, Barthélémy Heyrman, Dominique Ginhac

High dynamic range (HDR) imaging is still a challenging task in modern digital photography. Recent research proposes solutions that provide high-quality acquisition but at the cost of a very large number of operations and a slow inference time that prevent the implementation of these solutions on lightweight real-time systems. In this paper, we propose CEN-HDR, a new computationally efficient neural network by providing a novel architecture based on a light attention mechanism and sub-pixel convolution operations for real-time HDR imaging. We also provide an efficient training scheme by applying network compression using knowledge distillation. We performed extensive qualitative and quantitative comparisons to show that our approach produces competitive results in image quality while being faster than state-of-the-art solutions, allowing it to be practically deployed under real-time constraints. Experimental results show our method obtains a score of 43.04 mu-PSNR on the Kalantari2017 dataset with a framerate of 33 FPS using a Macbook M1 NPU.

Jonathan Crowther

Conversion of standard cameras to enable them to capture images in the ultraviolet (UV) and infrared (IR) spectral regions has applications ranging from purely artistic to science and research. Taking the modification of the camera a step further and removing the color filter array (CFA) results in the formation of a monochrome camera. The spectral sensitivities of a range of cameras with different sensors which were converted to monochrome were measured and compared with standard multispectral camera conversions, with an emphasis on their behavior from the UV through to the IR regions.

Santiago Delgado-Tirado, Alberto López-Miguel, Yazmin Báez-Peralta et al.

Abstract Background Prevalence of high myopia is continuously increasing, thus, patients affected with staphyloma are abundant worldwide. Assessment of the quality of vision in these patients is mandatory for a proper clinical counselling, specially when undergoing surgical procedures that require intraocular lenses implantation. Thus, the purpose of the study was to assess monochromatic higher order aberrations (HOAs) in highly myopic eyes with staphyloma with or without a dome-shaped macula. Methods Participants underwent spectral-domain optical coherence tomography, ocular axial biometry, dual Scheimpflug photography and integrated Placido disk topography, and Hartmann-Shack wavefront analysis. Five groups were evaluated: a low-moderate myopia control group (< 6.00 diopters, n = 31) and four high myopia (≥6.00 diopters) groups: eyes without staphyloma (n = 18), eyes with inferior staphyloma (n = 14), eyes with posterior staphyloma without dome-shaped macula (n = 15) and eyes with posterior staphyloma with dome-shaped macula (n = 17). Subsequently, two new groups (including all participants) were created to assess differences between myopia with and without staphyloma. One-way analysis of covariance was performed using age and lens densitometry as covariates. Results Statistically significant (p ≤ 0.05) differences in anterior corneal fourth-order HOAs were observed between the low-moderate myopia and no-dome-shaped macula (Mean: 0.16 μm) and dome-shaped macula posterior staphyloma groups (Mean: 0.12 μm) in younger patients (≤45 years old). The same groups also showed (p ≤ 0.05) significant differences for anterior corneal primary spherical aberration (Mean: 0.19 and 0.13 μm, respectively). In addition, anterior corneal tetrafoil was significantly higher (p = 0.04) in dome-shaped macula compared to no-dome-shaped macula (Mean: 0.18 vs 0.06 μm, respectively). When all participants were grouped together, significantly lower mean anterior corneal primary spherical aberration (0.15 μm vs. 0.27 μm, p = 0.004) and higher internal primary spherical aberration (0.04 μm vs. -0.06 μm, p = 0.04) was observed in staphyloma compared to no-staphyloma myopic patients. Conclusions Eyes with high myopia and staphyloma have less positive anterior corneal primary spherical aberration and less negative internal primary spherical aberration, suggesting that the anterior corneal surface tends to mimic in a specular fashion the posterior pole profile. This corneal behaviour appears to change in patients older than 45 years.

Michele Merlo

Edoardo Gellner never concealed the fact that he was a self-taught architect, although the references to the pragmatism of the Deutscher Werkbund and the theoretical teachings of the first Bauhaus are evident in his works. Drawing and photography are the tools through which Gellner educates his eye to look at the reality of things objectively and analytically. His method of analysis is based on a careful understanding of the territory and its matrices: from the comparison between military cartography and cadastral maps, Gellner draws a whole series of considerations on the historical, morphological, and social reasons that led to the development of such landscapes, and the study of centurial grids offers Gellner a counterproof to his theories. Thus, understanding the motivations of architecture but above all the origins of a landscape become the themes that Gellner deepens in an extensive series of studies for publications that were never completed that make these menabò real synthetic – or even artistic – visions of the landscape, as a tribute to the most perfect purovisibilist theory.

Ruizhi Shao, Gaochang Wu, Yuemei Zhou et al.

Cross-resolution image alignment is a key problem in multiscale gigapixel photography, which requires to estimate homography matrix using images with large resolution gap. Existing deep homography methods concatenate the input images or features, neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. In this paper, we consider the cross-resolution homography estimation as a multimodal problem, and propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs, namely, input images with different resolutions. The proposed local transformer adopts a local attention map specifically for each position in the feature. By combining the local transformer with the multiscale structure, the network is able to capture long-short range correspondences efficiently and accurately. Experiments on both the MS-COCO dataset and the real-captured cross-resolution dataset show that the proposed network outperforms existing state-of-the-art feature-based and deep-learning-based homography estimation methods, and is able to accurately align images under $10\times$ resolution gap.

Yunus Tansu Aksoy, Pinar Eneren, Erin Koos et al.

Existing energy balance models, which estimate maximum droplet spreading, insufficiently capture the droplet spreading from low to high Weber and Reynolds numbers and contact angles. This is mainly due to the simplified definition of the viscous dissipation term and incomplete modeling of the maximum spreading time. In this particular research, droplet impact on a smooth sapphire surface is studied for seven glycerol concentrations between 0% - 100%, and 294 data points are acquired using high-speed photography. Fluid properties such as density, surface tension, and viscosity are also measured. For the first time according to the authors' knowledge, we incorporate the fluid viscosity in the modeling of the maximum spreading time based on the recorded data. We also estimate the characteristic velocity of the viscous dissipation term in the energy balance equation. These viscosity-based characteristic scales help to formulate a more comprehensive maximum droplet spreading model. Thanks to this improvement, our model successfully fits the data available in the literature for various fluids and surfaces compared to the existing models.

Juan Wang, Bin Xia

The cup-to-disc ratio (CDR) is one of the most significant indicator for glaucoma diagnosis. Different from the use of costly fully supervised learning formulation with pixel-wise annotations in the literature, this study investigates the feasibility of accurate CDR measurement in fundus images using only tight bounding box supervision. For this purpose, we develop a two-task network named as CDRNet for accurate CDR measurement, one for weakly supervised image segmentation, and the other for bounding-box regression. The weakly supervised image segmentation task is implemented based on generalized multiple instance learning formulation and smooth maximum approximation, and the bounding-box regression task outputs class-specific bounding box prediction in a single scale at the original image resolution. To get accurate bounding box prediction, a class-specific bounding-box normalizer and an expected intersection-over-union are proposed. In the experiments, the proposed approach was evaluated by a testing set with 1200 images using CDR error and $F_1$ score for CDR measurement and dice coefficient for image segmentation. A grader study was conducted to compare the performance of the proposed approach with those of individual graders. The experimental results indicate that the proposed approach outperforms the state-of-the-art performance obtained from the fully supervised image segmentation (FSIS) approach using pixel-wise annotation for CDR measurement. Its performance is also better than those of individual graders. In addition, the proposed approach gets performance close to the state-of-the-art obtained from FSIS and the performance of individual graders for optic cup and disc segmentation. The codes are available at \url{https://github.com/wangjuan313/CDRNet}.

Wenyue Zhu, Jae Yee Ku, Yalin Zheng et al.

Much recent research focuses on how to make disease detection more accurate as well as “slimmer”, i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated. We propose a spatial explanatory modelling approach that combines Optical Coherence Tomography (OCT) retinal imaging data with clinical information. Our model consists of a spatial linear mixed effects inference framework, which innovatively models the spatial topography of key information via mixed effects and spatial error structures, thus effectively modelling the shape of the thickness map. We show that our spatial linear mixed effects (SLME) model outperforms traditional analysis-of-variance approaches in the analysis of Heidelberg OCT retinal thickness data from a prospective observational study, involving 300 participants with diabetes and 50 age-matched controls. Our SLME model has a higher power for detecting the difference between disease groups, and it shows where the shape of retinal thickness profiles differs between the eyes of participants with diabetes and the eyes of healthy controls. In simulated data, the SLME model demonstrates how incorporating spatial correlations can increase the accuracy of the statistical inferences. This model is crucial in the understanding of the progression of retinal thickness changes in diabetic maculopathy to aid clinicians for early planning of effective treatment. It can be extended to disease monitoring and prognosis in other diseases and with other imaging technologies.