The January 8-9, 2015 Frozen Uas Tour Blows Into Grand Forks, North Dakota A method for photogrammetric data reduction without the necessity for neither fiducial marks nor initial approximations for inner and outer orientation parameters of the camera has been developed. This approach is particularly suitable for reduction of data from non-metric photography, but has also distinct advantages in its application to metric photography. Preliminary fictitious data tests indicate that the approach is promising. Experiments with real data are underway.
This paper presents SCHEMA (Structured Components for Harmonized Engineered Modular Architecture), a structured prompt engineering methodology specifically developed for Google Gemini 3 Pro Image. Unlike generic prompt guidelines or model-agnostic tips, SCHEMA is an engineered framework built on systematic professional practice encompassing 850 verified API predictions within an estimated corpus of approximately 4,800 generated images, spanning six professional domains: real estate photography, commercial product photography, editorial content, storyboards, commercial campaigns, and information design. The methodology introduces a three-tier progressive system (BASE, MEDIO, AVANZATO) that scales practitioner control from exploratory (approximately 5%) to directive (approximately 95%), a modular label architecture with 7 core and 5 optional structured components, a decision tree with explicit routing rules to alternative tools, and systematically documented model limitations with corresponding workarounds. Key findings include an observed 91% Mandatory compliance rate and 94% Prohibitions compliance rate across 621 structured prompts, a comparative batch consistency test demonstrating substantially higher inter-generation coherence for structured prompts, independent practitioner validation (n=40), and a dedicated Information Design validation demonstrating >95% first-generation compliance for spatial and typographical control across approximately 300 publicly verifiable infographics. Previously published on Zenodo (doi:10.5281/zenodo.18721380).
Hasaan Maqsood, Saif Ur Rehman Khan, Sebastian Vollmer
et al.
Diabetic retinopathy screening traditionally relies on fundus photography, requiring specialized equipment and expertise often unavailable in primary care and resource limited settings. We developed and validated a deep learning (DL) system for automated diabetic classification using anterior segment ocular imaging a readily accessible alternative utilizing standard photography equipment. The system leverages visible biomarkers in the iris, sclera, and conjunctiva that correlate with systemic diabetic status. We systematically evaluated five contemporary architectures (EfficientNet-V2-S with self-supervised learning (SSL), Vision Transformer, Swin Transformer, ConvNeXt-Base, and ResNet-50) on 2,640 clinically annotated anterior segment images spanning Normal, Controlled Diabetic, and Uncontrolled Diabetic categories. A tailored preprocessing pipeline combining specular reflection mitigation and contrast limited adaptive histogram equalization (CLAHE) was implemented to enhance subtle vascular and textural patterns critical for classification. SSL using SimCLR on domain specific ocular images substantially improved model performance.EfficientNet-V2-S with SSL achieved optimal performance with an F1-score of 98.21%, precision of 97.90%, and recall of 98.55% a substantial improvement over ImageNet only initialization (94.63% F1). Notably, the model attained near perfect precision (100%) for Normal classification, critical for minimizing unnecessary clinical referrals.
Diego Luján Villarreal, Adriana Leticia Vera-Tizatl
The current study investigates the influence of intersubject variability in ocular characteristics on the mapping of visual field (VF) sites to the pointwise directional angles in retinal nerve fiber layer (RNFL) bundle traces. In addition, the performance efficacy on the mapping of VF sites to the optic nerve head (ONH) was compared to ground truth baselines. Fundus photographs of 546 eyes of 546 healthy subjects (with no history of ocular disease or diabetic retinopathy) were enhanced digitally and RNFL bundle traces were segmented based on the Personalized Estimated Segmentation (PES) algorithm’s core technique. A 24-2 VF grid pattern was overlaid onto the photographs in order to relate VF test points to intersecting RNFL bundles. The PES algorithm effectively traced RNFL bundles in fundus images, achieving an average accuracy of 97.6% relative to the Jansonius map through the application of 10th-order Bezier curves. The PES algorithm assembled an average of 4726 RNFL bundles per fundus image based on 4975 sampling points, obtaining a total of 2,580,505 RNFL bundles based on 2,716,321 sampling points. The influence of ocular parameters could be evaluated for 34 out of 52 VF locations. The ONH-fovea angle and the ONH position in relation to the fovea were the most prominent predictors for variations in the mapping of retinal locations to the pointwise directional angle (<i>p</i> < 0.001). The variation explained by the model (<i>R</i><sup>2</sup> value) ranges from 27.6% for visual field location 15 to 77.8% in location 22, with a mean of 56%. Significant individual variability was found in the mapping of VF sites to the ONH, with a mean standard deviation (95% limit) of 16.55° (median 17.68°) for 50 out of 52 VF locations, ranging from less than 1° to 44.05°. The mean entry angles differed from previous baselines by a range of less than 1° to 23.9° (average difference of 10.6° ± 5.53°), and RMSE of 11.94.
Photography, Computer applications to medicine. Medical informatics
The fundamental role of mathematics as an inspiration for artists, but also as a tool for art creation, is presented in this paper following different art fields, like architecture, sculpture, painting, photography, literature and poetry, movie making and music. The historical viewpoint is completed with recent applications of mathematics to create art in the digital era. Finally, the article contains a discussion about the possibility of the mathematical creation being considered artistic.
Medical image segmentation presents substantial challenges arising from the diverse scales and morphological complexities of target anatomical structures. Although existing Transformer-based models excel at capturing global dependencies, they encounter critical bottlenecks in multi-scale feature representation, spatial relationship modeling, and cross-layer feature fusion. To address these limitations, we propose the M<sup>3</sup>-TransUNet architecture, which incorporates three key innovations: (1) MSGA (Multi-Scale Gate Attention) and MSSA (Multi-Scale Selective Attention) modules to enhance multi-scale feature representation; (2) ME-MSA (Manhattan Enhanced Multi-Head Self-Attention) to integrate spatial priors into self-attention computations, thereby overcoming spatial modeling deficiencies; and (3) MKGAG (Multi-kernel Gated Attention Gate) to optimize skip connections by precisely filtering noise and preserving boundary details. Extensive experiments on public datasets—including Synapse, CVC-ClinicDB, and ISIC—demonstrate that M<sup>3</sup>-TransUNet achieves state-of-the-art performance. Specifically, on the Synapse dataset, our model outperforms recent TransUNet variants such as J-CAPA, improving the average DSC to 82.79% (compared to 82.29%) and significantly reducing the average HD95 from 19.74 mm to 10.21 mm.
Photography, Computer applications to medicine. Medical informatics
Ieda M. A. V. Dias, Antonio Jose Grande, Paulo T. C. Jardim
et al.
Abstract Background Climate change has severe health impacts, particularly for populations living in environmentally sensitive areas such as riversides, slopes, and forests. These challenges are exacerbated for Indigenous communities, who often face marginalisation and rely heavily on the land for their livelihoods. Despite their vulnerability, the perspectives of Indigenous populations on climate change and its impacts remain underexplored, creating a critical gap in the literature. This study explored the perceptions of Indigenous Brazilian university students on how climate change affects their daily lives and gathered their insights on potential adaptations to mitigate climate change-related impacts. Methods Using a participatory arts-based approach, participants captured photographs reflecting their lived experiences with climate change. Follow-up interviews provided a narrative framework for qualitative analysis, enabling participants to articulate the strengths and concerns of their communities while transcending cultural and linguistic barriers. Results The study revealed key themes, including (1) the fragility of ecosystems critical to Indigenous livelihoods, (2) the erosion of traditional knowledge systems due to environmental and social disruptions, and (3) the need for community-driven strategies to protect territories and preserve cultural identities. Participants highlighted the interconnectedness of their cultural values with environmental stewardship, emphasising the importance of maintaining these relationships as a form of resilience. Conclusion This study underscores the importance of protecting Indigenous territories and respecting their cultural identities to safeguard their survival and traditions. The voices of Indigenous university students provided valuable insights into community-based adaptations and strategies for mitigating the impacts of climate change.
ABSTRACT Objective To investigate the relationship between four metabolic indices—visceral adiposity index (VAI), lipid accumulation product (LAP), triglyceride glucose (TyG) index, and cholesterol‐HDL‐glucose (CHG) index—and the presence of diabetic nephropathy (DN) and diabetic retinopathy (DR) in patients with long‐standing type 2 diabetes mellitus (T2DM). Materials and Methods This prospective cross‐sectional study included 175 T2DM patients with disease duration >10 years who attended an endocrinology outpatient clinic between July 2021 and January 2022. DR was assessed via fundus photography, and DN was defined using the urinary albumin‐to‐creatinine ratio and eGFR. VAI, LAP, TyG, and CHG indices were calculated using anthropometric and biochemical parameters. Logistic regression was used to identify independent predictors. Results The mean age was 60 ± 10.1 years; 63.4% were female. DR and DN were observed in 50.3% and 38.9% of patients, respectively. VAI, LAP, and TyG were significantly higher in patients with DN but not with DR. CHG was elevated in both DN and DR (P < 0.05), and was the only independent predictor of DN (P = 0.005). Notably, CHG was significantly higher in proliferative vs non‐proliferative DR (P = 0.009), unlike the other indices. Conclusions While VAI, LAP, and TyG were associated only with nephropathy, CHG was linked to both DN and DR. Its integration of glycemic and lipid parameters may offer greater sensitivity for microvascular risk stratification in T2DM.
Diseases of the endocrine glands. Clinical endocrinology
The precise locations of unknown non-cooperative targets are a long-standing technical problem that needs to be solved urgently in disaster relief and emergency rescue. An imaging model of photography to a non-cooperative target was established based on the binocular vision forward intersection. The collinear equation representing the spatial position relationship between the target and its two images was obtained through coordinate system transformation, and the system of equations to calculate the geographic coordinates of the target was derived, which realized the geo-location of the unknown non-cooperative target with no control points and no source. The composition and source of the error of this target location method were analyzed, and the equation to calculate the total error of the target location was obtained according to the error synthesis theory. The accuracy of the target location was predicted. When the elevation difference between the camera and the target is 3 km, the location accuracy is 15.5 m. The same ground target was imaged by a certain type of aerial camera at different locations 3097 m above ground, and a target location verification experiment was completed. The longitude and latitude of the target obtained were compared with the true geographic longitude and latitude, and the location error of the verification experiment was calculated to be 16.3 m. The research work of this paper provides a theoretical basis and methods for the precise locations of unknown non-cooperative targets and proposes specific measures to improve the accuracy of target location.
At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability.
BackgroundThe assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis.MethodsThe LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability.ResultsIn testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision.ConclusionTo our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.
Objective To evaluate the safety and efficacy of skin care product containing artemisia naphtha in individuals with sensitive skin. Methods A single-center and self-controlled (before and after application) clinical trial was conducted in 31 subjects with sensitive skin. Test product was applied to the face of the subjects twice daily for 4 weeks. Measurement of physiological function, VISIA-CR photography and lactic acid test were carried out on the skin before and on the days 7,14,28 of the trial. 16S rRNA gene sequencing technology was used to analyze the microecological diversity of the skin surface meanwhile physician and subject' self-assessments were performed. Results Physician assessment scores for erythema were decreased at all three follow-up visits (P<0.05 vs. baseline). The subjects' self-assessments of itching, tingling, burning, tightness, flushing and sensitivity scores were also decreased (all P<0.05 vs. baseline).The skin a* values were decreased significantly (all P<0.05 vs. baseline). Similarly, erythema index was significantly decreased (all P<0.05 vs. baseline). Moreover, stratum corneum hydration levels were significantly increased on D7 and D28 (all P<0.05 vs. baseline). The transepidermal water loss rates tended to decrease, and were significantly different between the baseline and day 14 (P<0.05). In contrast, the skin elasticity and firmness parameters were not changed significantly. Furthermore, lactic acid scores were decreased significantly on D14 and D28 (all P<0.001 vs. baseline). Additionally, there was a tendency of increases in alpha diversity indices ACE and Chao1 on days D7 and D14, with a significant increase on day 14 (P<0.05 vs. baseline). Beta diversity analysis showed that the four groups of samples failed to gather into clusters, and the differences in composition structure were not significant. No adverse reactions were observed during the trial. Conclusions Skin care product containing artemisia naphtha is safe for individuals with sensitive skin and improves multiple clinical signs and symptoms of sensitive skin, including acceleration of the skin barrier recovery, elevation in stratum corneum hydration, reduction in transepidermal water loss rates and lactate stinging test score, alleviation of itching, tingling, burning, tightness and flushing, and increases in the richness and diversity of facial skin microbiota.
Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—<i>PSNR</i>, contrast-to-noise ratio—<i>CNR</i>, and structural similarity index metric—<i>SSIM</i>). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (<i>n</i> = 37) and the Optic Nerve Head (ONH) (<i>n</i> = 4). A wide range of image quality metrics was used; <i>PSNR</i> and <i>SNR</i> that ranged between 8 and 156 dB. The minority of studies (<i>n</i> = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a <i>PSNR</i> or <i>SNR</i> value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.
Photography, Computer applications to medicine. Medical informatics
We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emergence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthesize novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method.
Bokeh rendering is a popular and effective technique used in photography to create an aesthetically pleasing effect. It is widely used to blur the background and highlight the subject in the foreground, thereby drawing the viewer's attention to the main focus of the image. In traditional digital single-lens reflex cameras (DSLRs), this effect is achieved through the use of a large aperture lens. This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred. However, the hardware embedded in mobile phones is typically much smaller and more limited than that found in DSLRs. Consequently, mobile phones are not able to capture natural shallow depth-of-field photos, which can be a significant limitation for mobile photography. To address this challenge, in this paper, we propose a novel method for bokeh rendering using the Vision Transformer, a recent and powerful deep learning architecture. Our approach employs an adaptive depth calibration network that acts as a confidence level to compensate for errors in monocular depth estimation. This network is used to supervise the rendering process in conjunction with depth information, allowing for the generation of high-quality bokeh images at high resolutions. Our experiments demonstrate that our proposed method outperforms state-of-the-art methods, achieving about 24.7% improvements on LPIPS and obtaining higher PSNR scores.