Hasil untuk "Photography"

J. Rouse, R. H. Haas, D. Deering et al.

Andrew Jenkinson, Ben Jones, Lucy Chesson et al.

ABSTRACT Limited data exist describing how professional footballers meet their energy requirements during pre‐season, a phase characterised by increased training volume and a progressive shift from general conditioning to football‐specific preparation. This study quantified total, resting, and activity energy expenditure (AEE), diet‐induced thermogenesis, water turnover, and dietary intake in six professional male soccer players (age: 25 ± 1 year; height: 182.5 ± 10.1 cm; body mass: 77.8 ± 8.2 kg). Players were studied across 14 consecutive days, representing training‐only and training‐plus‐match microcycles. Total energy expenditure (TEE) was measured using doubly labelled water, resting energy expenditure (REE) by indirect calorimetry and dietary intake using the remote food photography method. Fourteen‐day mean TEE, REE, AEE and water turnover were 13.25 ± 1.31 MJ⋅day−1, 7.96 ± 0.89 MJ⋅day−1, 4.20 ± 1.03 MJ⋅day−1, 5.16 ± 0.66 L⋅day−1, respectively. Physical activity level was 1.67 ± 0.16 AU. Energy, carbohydrate, protein, and fat intakes were 10.95 ± 1.52 MJ⋅day−1, 2.8 ± 0.6 g⋅kg−1⋅day−1, 2.2 ± 0.4 g⋅kg−1⋅day−1, and 1.5 ± 0.4 g⋅kg−1⋅day−1, respectively. Total energy expenditure was not significantly different between training‐only and training‐plus‐match microcycles (+1.89 ± 1.98 MJ⋅day−1; ES = 0.95 ± 1.08; p = 0.100). No significant differences were observed in energy or macronutrient intake across weekly microcycles (p > 0.068) or between days (p > 0.144). Players did not achieve energy balance or align dietary intake with day‐to‐day training demands, suggesting limited nutrition periodisation during pre‐season. These findings highlight the need for practitioners to implement strategies supporting fuelling, recovery and adaptation during this critical phase.

Yuan-Kang Lee, Kuan-Lin Chen, Chia-Che Chang et al.

Nighttime color constancy still remains a challenging problem in computational photography due to low-light noise and complex illumination conditions. We present RL-AWB, a novel framework combining statistical methods with deep reinforcement learning for nighttime white balance. Our method begins with a statistical algorithm tailored for nighttime scenes, integrating salient gray pixel detection with novel illumination estimation. Building on this foundation, we develop the first deep reinforcement learning approach for color constancy that leverages the statistical algorithm as its core, mimicking professional AWB tuning experts by dynamically optimizing parameters for each image. To facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results show that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/

Ivan Kalynych, Mykola Karabiniuk, Mariia Nychvyd et al.

Problem Statement and Objective. The study aims to create a high-precision topographic framework for Lake Synevyr, one of the key internationally significant wetlands in the Carpathian region, by integrating remote sensing techniques and hydroacoustic bathymetric surveying. This approach has facilitated the development of a cartographic foundation for the implementation of long-term geoecological monitoring and the assessment of spatiotemporal changes in the lake system under increasing anthropogenic pressure and climate change. Methodology. The instrumental survey was conducted through a comprehensive mapping campaign using an unmanned aerial vehicle (UAV) equipped with a LiDAR system, photogrammetric processing of high-resolution digital imagery, and echosounding of the lakebed with GNSS referencing. The collected data were processed in specialized software environments (Terrasolid, Agisoft Metashape, Trimble HYDROpro, Digitals), enabling the generation of a digital terrain model, orthophotomap, TIN bathymetric model, and topographic plan. Study results. For the first time, a comprehensive high-accuracy instrumental mapping of Lake Synevyr has been carried out, including several key components: construction of a digital terrain model of the coastal area, creation of an orthophotomap with a ground sampling distance of 5.9 cm/pixel, and execution of bathymetric profiling with depth data obtained at an accuracy of ±0,1 m. As a result, the maximum lake depth of 19.98 m was recorded, a 3D model of the lakebed was constructed, and hypsometric profiles were developed. The acquired remote sensing and field survey data formed the basis of a high-quality 1:1 000 scale topographic map, which constitutes the core outcome of the study. Scientific novelty and practical significance. For the first time, a complex instrumental mapping of the mountainous Lake Synevyr has been implemented using an integrated approach combining LiDAR scanning, UAV-based aerial photography, and hydroacoustic bathymetric surveying with high-precision georeferencing. This enabled the acquisition of fundamentally new spatial information on the morphology of the lakebed and the creation of a multi-component geospatial data system and high-quality cartographic materials. The developed topographic plan and digital datasets provide a robust basis for further spatial analysis of natural changes and serve as a cartographic foundation for implementing long-term geoecological monitoring of Lake Synevyr. The data and cartographic products obtained enable highly accurate hydromorphological, landscape-ecological, and other scientific investigations, as well as the identification and monitoring of the dynamics of natural and anthropogenic processes. The proposed instrumental mapping methodology can be adapted for other sites in the Carpathian region, including within the framework of cross-border conservation initiatives and the systematic management of territories with special ecological status.

Fahmi Fairuzzaman, Sekar Diah Ayu Almira

Photographic works shared publicly on social media platforms, particularly Twitter, are not exempt from legal disputes, especially concerning copyright infringement. This research focuses on two central issues: first, the extent of copyright protection granted to photographs taken and uploaded by K-Pop fans on Twitter; and second, the legal remedies available to the rightful owners when their photographic works are used without permission. Using a normative legal research methodology, the study adopts a statutory approach, analyzing relevant legal rules and norms that apply to copyright protection. The research relies primarily on secondary legal sources, including laws, legal doctrines, journal articles, and relevant case studies. The findings indicate that photographic works shared via Twitter are protected under copyright law, which includes both moral rights—such as the right of attribution and integrity—and economic rights, including the right to reproduce and distribute the work. When such works are used without authorization, the original creators or rights holders have the option to pursue both litigation and non-litigation paths. Litigation may involve filing a civil or criminal case in the Commercial Court. Meanwhile, non-litigation solutions include various forms of alternative dispute resolution (ADR), such as mediation, arbitration, negotiation, conciliation, and consultation. This study highlights the legal vulnerabilities surrounding fan-created content in online spaces and underscores the importance of respecting copyright protections, even within fan communities. It also emphasizes the available legal pathways to protect the rights of content creators in digital environments.

Kangning Yang, Ling Ouyang, Huiming Sun et al.

Reflection removal technology plays a crucial role in photography and computer vision applications. However, existing techniques are hindered by the lack of high-quality in-the-wild datasets. In this paper, we propose a novel paradigm for collecting reflection datasets from a fresh perspective. Our approach is convenient, cost-effective, and scalable, while ensuring that the collected data pairs are of high quality, perfectly aligned, and represent natural and diverse scenarios. Following this paradigm, we collect a Real-world, Diverse, and Pixel-aligned dataset (named OpenRR-1k dataset), which contains 1,000 high-quality transmission-reflection image pairs collected in the wild. Through the analysis of several reflection removal methods and benchmark evaluation experiments on our dataset, we demonstrate its effectiveness in improving robustness in challenging real-world environments. Our dataset is available at https://github.com/caijie0620/OpenRR-1k.

Geoff Cox

What has replaced the still photograph are dynamic and distributed image-assemblages that unsettle received notions of space-time — no longer limited to traditional representation and not necessarily even visual. When it comes to computer vision for example, the descriptor photography seems largely redundant (despite deep learning computer vision systems being trained on large datasets of photographic images), and so too the tired metaphor of the eye that once supported its theories and practices. What is at stake here, as ever, is a kind of ‘seeing’ (if we continue to call it that) that makes clear what is visible, sensible and knowable, and crucially also what is not. We might call this seeing algorithmically, or seeing like a dataset, or perhaps even seeing like an infrastructure, comprised of fake images that render fake history. The logic of this invokes the complex notion of ‘image is dialectics at a standstill’, encapsulating a constellation of possible outcomes. To what extent is the radical potential that Benjamin once foresaw in montage applicable to image-based AI given that it seems less an instrument to imagine a qualitatively different future but simply more of the same. What can be seen is not so much representational nor photographic but latent traces of material relations and infrastructures that render historical experience in compromised form.

Stanislavsky Konstantin, Golovkov Ilya, Yusupova Sabina et al.

The presented paper investigates the application of HEC-RAS software for modelling of urban area waterlogging by surface rainfall runoff on the Black Sea coast of Russia. The authors assess the risk of emergencies caused by rainfall of different intensities. Using a digital elevation model and an orthophotoplane obtained by airborne laser scanning and aerial photography, the territory and surface types are analyzed. The results of the study identify sensitive areas and consider the feasibility of constructing barrier structures to manage surface runoff. Data on the capacity of the existing pipe and its effect on runoff accumulation are analyzed. The study is of practical relevance for risk assessment and emergency prevention measures related to surface runoff in urban environments on the Black Sea coast.

Mohsen Amiri, Mehdi Hosseinzadeh

The growing potential of quadcopters in various domains, such as aerial photography, search and rescue, and infrastructure inspection, underscores the need for real-time control under strict safety and operational constraints. This challenge is compounded by the inherent nonlinear dynamics of quadcopters and the on-board computational limitations they face. This paper aims at addressing these challenges. First, this paper presents a comprehensive procedure for deriving a linear yet efficient model to describe the dynamics of quadrotors, thereby reducing complexity without compromising efficiency. Then, this paper develops a steady-state-aware Model Predictive Control (MPC) to effectively navigate quadcopters, while guaranteeing constraint satisfaction at all times. The main advantage of the steady-state-aware MPC is its low computational complexity, which makes it an appropriate choice for systems with limited computing capacity, like quadcopters. This paper considers Parrot Bebop 2 as the running example, and experimentally validates and evaluates the proposed algorithms.

Hui Lin, Charilaos Apostolidis, Aggelos K. Katsaggelos

Differences in image quality, lighting conditions, and patient demographics pose challenges to automated glaucoma detection from color fundus photography. Brighteye, a method based on Vision Transformer, is proposed for glaucoma detection and glaucomatous feature classification. Brighteye learns long-range relationships among pixels within large fundus images using a self-attention mechanism. Prior to being input into Brighteye, the optic disc is localized using YOLOv8, and the region of interest (ROI) around the disc center is cropped to ensure alignment with clinical practice. Optic disc detection improves the sensitivity at 95% specificity from 79.20% to 85.70% for glaucoma detection and the Hamming distance from 0.2470 to 0.1250 for glaucomatous feature classification. In the developmental stage of the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge, the overall outcome secured the fifth position out of 226 entries.

Weiyi Zhang, Siyu Huang, Jiancheng Yang et al.

Fundus Fluorescein Angiography (FFA) is a critical tool for assessing retinal vascular dynamics and aiding in the diagnosis of eye diseases. However, its invasive nature and less accessibility compared to Color Fundus (CF) images pose significant challenges. Current CF to FFA translation methods are limited to static generation. In this work, we pioneer dynamic FFA video generation from static CF images. We introduce an autoregressive GAN for smooth, memory-saving frame-by-frame FFA synthesis. To enhance the focus on dynamic lesion changes in FFA regions, we design a knowledge mask based on clinical experience. Leveraging this mask, our approach integrates innovative knowledge mask-guided techniques, including knowledge-boosted attention, knowledge-aware discriminators, and mask-enhanced patchNCE loss, aimed at refining generation in critical areas and addressing the pixel misalignment challenge. Our method achieves the best FVD of 1503.21 and PSNR of 11.81 compared to other common video generation approaches. Human assessment by an ophthalmologist confirms its high generation quality. Notably, our knowledge mask surpasses supervised lesion segmentation masks, offering a promising non-invasive alternative to traditional FFA for research and clinical applications. The code is available at https://github.com/Michi-3000/Fundus2Video.

Yunfan Lu, Yijie Xu, Wenzong Ma et al.

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model's applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://github.com/yunfanLu/ev-demosaic

Nazgul Zh. ZHENSIKBAYEVA, Nazym K. KABDRAKHMANOVA, Aigul Y. YEGINBAYEVA et al.

In this article, a study was conducted to analyze the factors leading to the occurrence of one of the natural disasters - fires on the territory of Eastern Kazakhstan. This work examined the consequences of forest fires that occurred before 2022 and analyzed changes in the state of forest cover in recent years using satellite images. The article also describes the methodology and application of geographic information technologies for assessing the potential damage caused by fires based on data from space. This technology provides a quick assessment of possible damage from forest and steppe fires, which can be supplemented with data from the area. Based on space monitoring data, areas affected by fires are identified, and a rapid assessment of such areas is carried out using information from the MODIS system, after which it is recommended to supplement it with more detailed medium-resolution data, such as Landsat images. In addition, the article determined the structure of forest cover, and also identified factors influencing the occurrence of fire conditions in the territory of Eastern Kazakhstan. As a result of the study, a set of proposals was developed to assess the level of damage caused by forest fires and measures to prevent such fires.

Walter Dellisanti, Walter Dellisanti, Jeffery T. H. Chung et al.

Scleractinian corals are increasingly subjected to local stressors combined with global changes. In subtropical areas, corals exhibit metabolic plasticity and resilience in response to variability and extremes in local temperature, salinity, and light; however, the physiological mechanisms by which corals acclimate or adapt to these changing conditions remain disputed. We assessed the physiological status of the coral Platygyra carnosa during a two-year in situ monitoring survey. To obtain metabolic rates (respiration and photosynthesis), photochemical efficiency (Fv / Fm), and biocalcification measurements, non-invasive techniques such as underwater respirometry, Pulse Amplitude Modulated (PAM) fluorometry, total alkalinity measurements, and digital photography were used. Our findings show clear seasonality in water quality parameters, which affected coral health. Elevated temperatures during the summer were below the maximum monthly mean < 31°C) but reduced the energetic productivity of corals (-44% relative to winter). Fluctuations in salinity (25–38 ppt) and pH (7.65–8.44) were linked to rainfall and reduced calcification rates. The conditions during the spring were favorable for coral metabolism and calcification (+20% relative to summer). Overall, our research demonstrates that the metabolic plasticity of P. carnosa in response to shifts in seawater quality allows this species to survive ongoing environmental change. Our in situ observations provide fundamental insights into coral response mechanisms under changing environmental conditions and contribute to projections of coral health under future scenarios of global change.

Marcin Pliszka

The article is dedicated to the discussion of Dzień na ziemi [A Day on Earth] by Michał Paweł Markowski, a book consisting, according to the author, of closely related parts: a fragment of novel prose, an essay on travel, and a collection of photographs. The spirit of the work is therefore one of an experimental lyrical-visual whole, with non-apparent rules on how it should be read. At the same time, it fits into the long tradition of iconotextual syncretism, though simultaneously, the presence of photographs determines its character. The often-problematised point of a photographic image, situating a photograph in the, impossible to reconcile, opposition of document and curated work of art, is further complicated in the work by Markowski due to the decision made by the author, who labelled all elements of his piece as ‘prose’, while accentuating their narrative aspect at the same time. Although photography is not the medium of epic, its textual contextualisations release narrative mechanisms, placing the task of rendering the non-uniform textual-visual material cohesive onto the reader. The author of the study aims at analysing the way in which photography functions in the artistic whole created by Markowski. Photographs are present in the work in many ways: on a material level as an image embedded in the text, on a textual level as a story of the author of the picture, on a meta-level as an object of reflection, but also in an autonomous aspect, as a photography cycle, as well as commentary on the text and an element of the book’s visual design. The article analyses the mutual relations between word and image, interpretative procedures leading in different directions, as well as photographic-textual addenda and dialogue.

James Weng, Niklas B. Thompson, Christopher Folmar et al.

To obtain the best resolution for any measurement there is an ever-present challenge to achieve maximal differentiation between signal and noise over as fine of sampling dimensions as possible. In diffraction science these issues are particularly pervasive when analyzing small crystals, systems with diffuse scattering, or other systems in which the signal of interest is extremely weak and incident flux and instrument time is limited. We here demonstrate that the tool of compressed sensing, which has successfully been applied to photography, facial recognition, and medical imaging, can be effectively applied to diffraction images to dramatically improve the signal-to-noise ratio (SNR) in a data-driven fashion without the need for additional measurements or modification of existing hardware. We outline a technique that leverages compressive sensing to bootstrap a single diffraction measurement into an effectively arbitrary number of virtual measurements, thereby providing a means of super-resolution imaging.

Mingyang Xie, Matthew Chan, Christopher Metzler

High dynamic range (HDR) images are important for a range of tasks, from navigation to consumer photography. Accordingly, a host of specialized HDR sensors have been developed, the most successful of which are based on capturing variable per-pixel exposures. In essence, these methods capture an entire exposure bracket sequence at once in a single shot. This paper presents a straightforward but highly effective approach for turning an off-the-shelf polarization camera into a high-performance HDR camera. By placing a linear polarizer in front of the polarization camera, we are able to simultaneously capture four images with varied exposures, which are determined by the orientation of the polarizer. We develop an outlier-robust and self-calibrating algorithm to reconstruct an HDR image (at a single polarity) from these measurements. Finally, we demonstrate the efficacy of our approach with extensive real-world experiments.

Dean Hazineh, Soon Wei Daniel Lim, Qi Guo et al.

Optical metasurfaces composed of precisely engineered nanostructures have gained significant attention for their ability to manipulate light and implement distinct functionalities based on the properties of the incident field. Computational imaging systems have started harnessing this capability to produce sets of coded measurements that benefit certain tasks when paired with digital post-processing. Inspired by these works, we introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure. We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums across the four polarization channels, independent of the spatial filter size. In contrast to previous work on incoherent opto-electronic filtering that can realize only one spatial filter, our approach can realize a continuous family of filters from a single capture, with filters being selected from the family by adjusting the post-capture digital summation weights. To find a metasurface that can realize a set of user-specified spatial filters, we introduce a form of gradient descent with a novel regularizer that encourages light efficiency and a high signal-to-noise ratio. We demonstrate several examples in simulation and with fabricated prototypes, including some with spatial filters that have prescribed variations with respect to depth and wavelength. Visit the Project Page at https://deanhazineh.github.io/publications/Multi_Image_Synthesis/MIS_Home.html

Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff et al.

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference with informative priors to handle the ill-posedness of such problems. Amongst such priors, score-based generative models (SGM) have recently been successfully applied to several different inverse problems. In this study, we exploit the particular structure of the prior defined by the SGM to define a sequence of intermediate linear inverse problems. As the noise level decreases, the posteriors of these inverse problems get closer to the target posterior of the original inverse problem. To sample from this sequence of posteriors, we propose the use of Sequential Monte Carlo (SMC) methods. The proposed algorithm, MCGDiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting.

Ali Reza Omrani, Davide Moroni

Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography has seen profound changes recently. To be more specific, such methods try to reconstruct the lost luminosity of the real world caused by the limitation of regular cameras from the Low Dynamic Range (LDR) images. Additionally, although the State-Of-The-Art methods in this topic perform well, they mainly concentrate on combining different exposures and have less attention to extracting the informative parts of the images. Thus, this paper aims to introduce a new model capable of incorporating information from the most visible areas of each image extracted by a visual attention module (VAM), which is a result of a segmentation strategy. In particular, the model, based on a deep learning architecture, utilizes the extracted areas to produce the final HDR image. The results demonstrate that our method outperformed most of the State-Of-The-Art algorithms.