Photographic Images Research Articles

Contribution to the hyaliodine fauna (Miridae: Deraeocorinae: Hyaliodini) of New Caledonia with a description of two new species and a checklist of New Caledonian plant bugs

Two new species of the Montagneria Akingbohungbe, 1978—M. barbarae sp. nov. and M. nataliae sp. nov.—are diagnosed and described. Photographic images of habitus and genital structures, as well as scanning electron micrographs of the selected structures of both species are provided. The gland openings located on the thorax are figured and discussed. New records of three hyaliodine species, Femurocoris spinosus Carvalho, 1977, Montagneria cuneatus (Distant, 1920), and M. nigroscutellatum (Distant, 1920) in New Caledonia are also presented. Moreover, a checklist of the Miridae species of New Caledonia is given.

Using Image Analysis Technique for Predicting Light Lamb Carcass Composition.

Over the years, numerous techniques have been explored to assess the composition and quality of sheep carcasses. This study focuses on the utilization of video image analysis (VIA) to evaluate the composition of light lamb carcasses (4.52 ± 1.34 kg, mean cold carcass weight ± SD). Photographic images capturing the lateral and dorsal sides of fifty-five light lamb carcasses were subjected to analysis. A comprehensive set of measurements was recorded, encompassing dimensions such as lengths, widths, angles, areas, and perimeters, totaling 21 measurements for the lateral view images and 29 for the dorsal view images. K-Folds stepwise multiple regression analyses were employed to construct prediction models for carcass tissue weights (including muscle, subcutaneous fat, intermuscular fat, and bone) and their respective percentages. The most effective prediction equations were established using data from cold carcass weight (CCW) and measurements from both dorsal and lateral views. These models accounted for a substantial portion of the observed variation in the weights of all carcass tissues (with K-fold-R2 ranging from 0.83 to 0.98). In terms of carcass tissue percentages, although the degree of variation explained was slightly lower (with K-fold-R2 ranging from 0.41 to 0.78), the VIA measurements remained integral to the predictive models. These findings underscore the efficacy of VIA as an objective tool for assessing the composition of light lamb carcasses, which are carcasses weighing ≈ 4-8 kg.

Patterns of Progression of Nonproliferative Diabetic Retinopathy Using Non-Invasive Imaging.

To identify progression of nonproliferative diabetic retinopathy (NPDR) in patients with type 2 diabetes by combining optical coherence tomography angiography (OCTA) metrics and color fundus photography (CFP) images. This study was a post hoc analysis of a prospective longitudinal cohort study (CORDIS, NCT03696810) with 2-year duration. This study enrolled 122 eyes. Ophthalmological examinations included OCTA and CFP. OCTA metrics included skeletonized vessel density (SVD) and perfusion density (PD) at the superficial capillary plexus (SCP) and deep capillary plexus (DCP). Microaneurysm turnover analysis and Early Treatment Diabetic Retinopathy Study (ETDRS) grading for diabetic retinopathy (DR) severity assessment were performed on 7-field CFP. Eyes graded as ETDRS level 20 showed significant capillary nonperfusion predominantly in the inner ring area in the SCP (P < 0.001), whereas eyes graded as ETDRS level 35 and ETDRS levels 43 and 47 showed significant capillary nonperfusion in both the SCP and DCP in both inner and outer rings (P < 0.001). When evaluating rates of progression in capillary nonperfusion for the 2-year period of follow-up, changes were found predominantly in the DCP for SVD and PD and were better identified in the outer ring area. Microaneurysm turnover contributes to the characterization of NPDR progression by discriminating ETDRS level 35 from ETDRS levels 43 and 47 (P < 0.001), which could not be achieved using only OCTA metrics. Patterns of progression of NPDR can be identified combining OCTA examinations of the superficial and deep retinal capillary plexi of central retina and determination of microaneurysm turnover from fundus photographs. Our study reports results from a registered clinical trial that advances understanding of disease progression in NPDR.

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

Assessment of Dust Deposition through Image Analysis in Complex and Remote Exhibition Sites: Study in the Cloister of the Santa María de El Paular Monastery in the Sierra de Guadarrama, Spain

Dust deposition is an important aspect of the conservation of heritage collections. Most proposed methods for dust monitoring focus on total area coverage or airborne concentrations. There is a lack of published data and methodologies to obtain size distributions of deposited particles on real historic sites. The purpose of the study was to develop and describe a method for obtaining quantitative data from dust deposition without the need for sophisticated laboratory equipment, based on optical microscopy photography and software-based image analysis. Bare microscope slides were used as passive collectors of dust. Tests were carried out on a collection of oil paintings displayed in the cloister of the Monastery of Santa María de El Paular in the Sierra de Guadarrama in Spain for one year, with a distance of 100 km between this place and the laboratory. The designed method allowed for the detection of significant differences in deposition depending on the location and seasonal period. Vertically orientated bare slides did not provide relevant information and a magnification of 50× was not really useful. However, horizontal collectors and a magnification of 10× allowed studying the deposition of particles above 2 μm2.

Simple and sensitive electrochemical sensing of amethopterin by using carbon nanobowl/cyclodextrin electrode

Resulted from the severe side effects, the development of inexpensive, simple and sensitive method for amethopterin (ATP, an antineoplastic drug) is very important but it still remains a challenge. In this work, low cost nanohybrid composed of carbon nanobowl (CNB) and β-cyclodextrins (β-CD) (CNB-CD) was prepared with a simple autopolymerization way and applied as electrode material to develop a novel electrochemical sensor of ATP. Scanning-/transmission-electron microscopy, Fourier transform infrared spectrum, photographic image and electrochemical technologies were utilized to characterize morphologies and structure of the as-prepared CNB and CNB-CD materials. On the basic of the coordination advantages from CNB (prominent electrical property and surface area) and β-CD (superior molecule-recognition and solubility capabilities), the CNB-CD nanohybrid modified electrode exhibits superior sensing performances toward ATP, and a low detection limit of 0.002 μM coupled with larger linearity of 0.005–12.0 μM are obtained. In addition, the as-prepared sensor offers desirable repeatability, stability, selectivity and practical application property, confirming that this proposal may have important applications in the determination of ATP.

Impact of Solar Eclipses on Vision: Insights from Optical Coherence Tomography and Optical Coherence Tomography Angiography Analysis*

Purpose Solar retinopathy, resulting from solar eclipse exposure, poses risks to visual health. This study explores acute and chronic phase findings using clinical examinations and optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) with a focus on longitudinal assessment. Methods Seven eyes with a history of unprotected solar eclipse exposure were included. Clinical examination, fundus photography, OCT, and OCT-A imaging were performed at initial assessment, as well as at one-month and six-month follow-up intervals. Data analysis included descriptive statistics. Results The cases, exposed without protection, underwent assessments, revealing variable visual acuity, outer retinal layer, and Henle fiber layer changes during follow-up. Regression of hyperreflectivity within the outer retinal and Henle fiber layers was observed over time in all eyes, although persistent microdefects within the outer retinal layer were noted in specific cases. OCT-A imaging revealed a larger foveal avascular zone, which persisted over a six-month period in select cases. Additionally, affected eyes exhibited a decrease in superficial vascular density, with subsequent improvement noted during the six-month period. Conclusion Solar retinopathy can result in visual impairment, accompanied by alterations observed in the Henle fiber layer using OCT. Additionally, OCT-A findings indicate possible vascular involvement. This study underscores the significance of adopting protective measures during solar eclipses and emphasizes the value of employing longitudinal multimodal imaging techniques to comprehend the pathophysiology of the condition.

Patterned, plain, and in-between: An assessment of ecogeographic divergence between colour pattern morphs of the common egg-eater Dasypeltis scabra

ABSTRACT Intraspecific variation in colour patterns may reflect adaptive responses to local environmental regimes that favour selection of different trade-offs between visual communication, thermoregulation, and anti-predatory functions. Understanding the drivers of colour pattern variation within species can therefore provide valuable insights about adaptation. However, the ecogeographic correlates, and thus the ecological drivers, associated with the segregation of different colour morphs for most reptile species, including snakes, are not well known. Here, we examined differences in environmental niches associated with occurrence records of preserved museum specimens and georeferenced photographic images of common egg-eaters (Dasypeltis scabra) to characterise ecogeographic divergence between typically patterned, patternless, and faintly marked individuals. We gathered 1707 records that we assigned as either patterned, plain, or intermediate morphs. Most records were of patterned individuals, which were widely distributed in southern Africa. Climate niches differed between morphs as plain and intermediate snakes were restricted to high-elevation areas in the Highveld grasslands of South Africa, and woodland-dominated areas in the central and eastern parts of both Zambia and Zimbabwe, but there were no areas where only plain or intermediate snakes occurred in isolation from patterned snakes. Environmental niche modelling predicted minimal areas suitable for co-habitation of colour morphs. We speculate that plain or weakly-marked colour patterns likely developed as an adaptation to increase camouflage ability in open areas in grassland and certain woodland habitats.

3D scanning of daguerreotypes

Daguerreotypes are historical photographic images made on mirror-like metallic plates. These are heritage objects whose shape cannot be measured with invasive techniques, like contact probes, but the high reflectivity of their surfaces makes the use of non-invasive, 3D-measuring optical techniques challenging. Moreover, the dark areas resulting from their degradation produce a very high contrast, which add extra difficulties to their measurement. In the last few years, several strategies have been developed to overcome the limitations of optical techniques when measuring reflective metallic surfaces. Many of these solutions are not applicable to the study of cultural heritage artifacts, as they are invasive. We attempted the use of conoscopic holography in a 3D-scanning system using a double-exposure strategy. This is a promising option for 3D measuring of daguerreotypes, as we experimentally demonstrated in this work. We present the results obtained from the analyses of two 19th-century daguerreotypes with different superficial conditions. The double-exposure allowed us to obtain high-quality data from the entire object surface. This enabled the measurement of micro-scale details related to the manufacturing process and/or to the corrosion deposits. The proposed methodology can be exploited to monitor the overall health of highly reflective metallic objects but also the outcomes of some conservation treatments, such as cleaning.

Watching Photographs: Inhabiting a Queer Feminist Perspective to Contest Reproductions of the ‘Refugee Crisis’

ABSTRACT Journalists have claimed that the 2015 European ‘refugee crisis’ is one of the most photographed crises in human history. It is important to understand what kind of messages these photographs convey, how they reproduce the hegemonic narrative, and how they become commodities. In this paper, we critique the visual economy of the ‘refugee crisis’ from a queer feminist perspective through the lens of reproduction. We show how the reproduction of images of ‘refugees’ is structured by, and naturalises reproductive heteronormativity, amongst other institutions, which we argue is essential to the functioning of bordered nation-states: the figure of the refugee is reproduced as presumptively heterosexual, and as reproductive. We focus on watching photographic images because, as we have seen over the past six years, through the use and control of photography, states seek to generate consent to the necropolitical management of ‘crisis’, to the undeclared war against refugees. We argue that by watching photographs, as opposed to merely looking at them, we can inhabit a queer feminist perspective that seeks to abolish state-imposed divisions between ‘refugees’, ‘migrants’, and ‘citizens’.

Curved Polymeric Sandwich Composites Subjected to Air Shock: An Experimental Investigation

BackgroundThe vulnerability of polymeric composite sandwich structures in marine applications to air explosions highlights a significant gap in our understanding of the dynamic behavior of the curved sandwich structures, which is essential for design improvements.ObjectiveThis study aims to explore the dynamic response and failure mechanisms of curved sandwich composite panels subjected to air-blast loading, providing insights into their structural integrity under such conditions.MethodsExperiments were performed using laboratory-simulated air shocks generated by a shock tube, employing high-speed photography and digital image correlation to measure deflections on the back surface of the panels. The panels, made with PVC closed-cell foam cores of two densities (H45 and H130), were tested across three curved geometries (radii of 112 mm, 305 mm, and infinity) under various boundary conditions.ResultsFindings indicate an increase in deformation with a decreased radius of curvature under simple support conditions, a trend that reverses under arrested displacement conditions. Moreover, a reduced radius significantly enhances panel strength and resistance to interfacial damage, with the primary failure mode transitioning from core shear cracking to interfacial debonding as core density increases.ConclusionsThe study reveals that the radius of curvature, boundary conditions, and core density significantly affect curved sandwich panels’ dynamic response and performance. Panels with smaller radii and higher core densities exhibit increased strength, though boundary conditions introduce variable effects on deformation behavior.

Prevalence of Forward Head Posture in Goldsmith Workers

Background: It was found that goldsmiths were highly affected by improper body posture and workload. Twisting, bending over, and over-reaching are the results of poorly designed workstations; this will cause postural abnormality, and later on, this can lead to musculoskeletal-related disorders, in which forward head posture (FHP) is the most common postural abnormality. Therefore, to aware the workers about their musculoskeletal disorder as well as the analysis of the worker suffering from forward head posture (FHP). Materials and Methods: The study took place at the workplace of goldsmith workersin Nagpur, Maharashtra, for 1.5 years. With the confidence interval of 95%, an absolute precisionof 15and based on a pilot study and literature reviews, the sample size was estimated to be 35.AGoniometer, camera(for photographic Images), laptop and two-sided tape were used to determine the cranio-vertebral angle. Thirty-five goldsmiths participated in the study. The photographic method was used to evaluate cranio-vertebral angle, which helps to determine forward head posture. Result:In this study 35 goldsmith workers were evaluated with a mean age of 37±10 years, a mean working hour of 11±10 hours and a work experience of 18±9.8 years. Out of 35 goldsmiths 25 showed the prevalence of forward head posture, or 71.42% of them had forward head posture. Conclusion: Based on the results the present study which was conducted on goldsmith workers concluded that prevalence of forward head posture was 71.42% among the population

Experimental study of the dynamics and mass transfer of single CO2 bubble accompanied by reactions in Hele-Shaw cell

The reduction of carbon emissions has become a critical global issue, and the use of monoethanolamine (MEA) solution for CO2 absorption is prevalent in industry. To elucidate the mass transfer mechanisms in reactive multiphase flow, we employed high-speed photography and digital image processing to examine the dynamics and mass transfer behavior of CO2 bubbles in a Hele-Shaw cell. The results indicate that as the MEA solution concentration increases, oscillations during bubble ascent diminish, and the terminal velocity decreases. Based on changes in the mass transfer coefficient, the reaction process can be segmented into a phase of intensified mass transfer, marked by a rapid decrease in bubble equivalent diameter, and a phase of deteriorating mass transfer, where the diameter stabilizes. Additionally, we introduced a dimensionless mathematical model for the Sherwood number based on experimental findings, and its reliability was confirmed.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Fractal dimension of the morphology on differently oriented surfaces of coal preparations

Choosing the surface of a lump section was justified to determine the degree of metamorphism by the index of gray shade intensity. The main method is optical spectroscopy using a video-optical complex MBI-11, NB 200 with subsequent digital computer processing and calculation of the fractal dimension (Scope photo software) of microphoto images of coal matter preparations by lump sections. The values for the index of gray shade intensity of photographic images of surfaces (section, bedding, cut) for lump sections based on samples of coals of various grades were analyzed. It has been established that the average values of the degree of metamorphism indices for different surfaces of the preparation differ, which is confirmed by low correlation coefficients: 0.585 (section-bedding), 0.666 (section-cut), 0.462 (cut-bedding). It has been found that the fractal dimension, which characterizes the complexity of the morphology of the surfaces under study, reflects an increase in the amount and orderliness of hydrocarbons associated with a change in the degree of metamorphism. It was proven that its most natural decrease related to an increase in the degree of metamorphism, is observed on surfaces perpendicular to the bedding (section).

SketchTrans: Disentangled Prototype Learning With Transformer for Sketch-Photo Recognition.

Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality. Existing works tend to learn shared embedding spaces with CNN models by discarding the appearance cues for photo images or introducing GAN for sketch-photo synthesis. The former unavoidably loses discriminability, while the latter contains ineffaceable generation noise. In this paper, we start the first attempt to design an information-aligned sketch transformer (SketchTrans +) via cross-modal disentangled prototype learning, while the transformer has shown great promise for discriminative visual modelling. Specifically, we design an asymmetric disentanglement scheme with a dynamic updatable auxiliary sketch (A-sketch) to align the modality representations without sacrificing information. The asymmetric disentanglement decomposes the photo representations into sketch-relevant and sketch-irrelevant cues, transferring sketch-irrelevant knowledge into the sketch modality to compensate for the missing information. Moreover, considering the feature discrepancy between the two modalities, we present a modality-aware prototype contrastive learning method that mines representative modality-sharing information using the modality-aware prototypes rather than the original feature representations. Extensive experiments on category- and instance-level sketch-based datasets validate the superiority of our proposed method under various metrics.

Improving Target Geolocation Accuracy with Multi-View Aerial Images in Long-Range Oblique Photography

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation.

Implementation of Convolutional Neural Network (CNN) Method for Fish Processed Cuisine Image Identification Application with Google Maps Features

The rapid advancement of science and technology encourages dynamic transformation in various sectors, especially in the field of information and communication technology (ICT), especially with the existence of Android-based smartphones. This advancement revolutionizes the way we access information, especially about various processed fish dishes in Indonesia. However, despite the plethora of culinary offerings, travelers often find it difficult to discover traditional dishes through social media platforms. To bridge this gap, a new app that utilizes artificial neural networks has been developed. The app allows users to photograph and upload images of processed fish dishes to recognize and provide detailed descriptions and recipes. In addition, integrating Google Maps makes it easy for users to find nearby places that serve these dishes. Testing the app with a dataset consisting of 1577 images of six types of processed fish dishes yielded promising results, with accuracy reaching 97.57% over 120 epochs. This innovation not only preserves cultural heritage but also enhances the culinary experience for locals and tourists.

Detection of Diabetic Foot Ulcers Using Dense Net Framework

Diabetic Foot Ulcers (DFUs) pose a significant health care challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. This study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.

КОМПЛЕКСНІ ОНТОЛОГІЧНІ ТА НЕЙРОМЕРЕЖЕВІ МОДЕЛІ ФОТОГРАФІЧНИХ ОБРАЗІВ

This article explores an innovative approach to developing computer vision systems, based on the deep integration of complex ontological and neural network models for effective analysis of photographic images. This work proposes a novel hybrid method in the context of contemporary challenges associated with image recognition, such as a high diversity of classes and problems with the occlusion of objects in low-quality photos. This method provides computer vision models with a deeper understanding of the context and semantics of images, utilizing ontological structures to represent visual content. The analytical process includes the identification of key objects in the image and the determination of their ontological context, allowing neural networks to effectively perform a gradient transformation of the input data for more accurate recognition and classification. The proposed model demonstrates the potential for gaining an advantage over traditional methods in computer vision tasks, opening new possibilities for expanding computer vision applications in scientific, industrial, and domestic spheres.