Conventional Camera Research Articles

Validation of Angle Estimation Based on Body Tracking Data from RGB-D and RGB Cameras for Biomechanical Assessment.

Motion analysis is an area with several applications for health, sports, and entertainment. The high cost of state-of-the-art equipment in the health field makes it unfeasible to apply this technique in the clinics' routines. In this vein, RGB-D and RGB equipment, which have joint tracking tools, are tested with portable and low-cost solutions to enable computational motion analysis. The recent release of Google MediaPipe, a joint inference tracking technique that uses conventional RGB cameras, can be considered a milestone due to its ability to estimate depth coordinates in planar images. In light of this, this work aims to evaluate the measurement of angular variation from RGB-D and RGB sensor data against the Qualisys Tracking Manager gold standard. A total of 60 recordings were performed for each upper and lower limb movement in two different position configurations concerning the sensors. Google's MediaPipe usage obtained close results compared to Kinect V2 sensor in the inherent aspects of absolute error, RMS, and correlation to the gold standard, presenting lower dispersion values and error metrics, which is more positive. In the comparison with equipment commonly used in physical evaluations, MediaPipe had an error within the error range of short- and long-arm goniometers.

Design of Airborne Large Aperture Infrared Optical System Based on Monocentric Lens.

Conventional reconnaissance camera systems have been flown on manned aircraft, where the weight, size, and power requirements are not stringent. However, today, these parameters are important for unmanned aerial vehicles (UAVs). This article provides a solution to the design of airborne large aperture infrared optical systems, based on a monocentric lens that can meet the strict criteria of aerial reconnaissance UAVs for a wide field of view (FOV) and lightness of airborne electro-optical pod cameras. A monocentric lens has a curved image plane, consisting of an array of microsensors, which can provide an image with 368 megapixels over a 100° FOV. We obtained the initial structure of a five-glass (5GS) asymmetric monocentric lens with an air gap, using ray-tracing and global optimization algorithms. According to the design results, the ground sampling distance (GSD) of the system is 0.33 m at 3000 m altitude. The full-field modulation transfer function (MTF) value of the system is more than 0.4 at a Nyquist frequency of 70 lp/mm. We present a primary thermal control method, and the image quality was steady throughout the operating temperature range. This compactness and simple structure fulfill the needs of uncrewed airborne lenses. This work may facilitate the practical application of monocentric lens in UAVs.

Location of Latent Forensic Traces Using Multispectral Bands

In this paper, a conventional camera modified to capture multispectral images, has been used to locate latent forensic traces with a smart combination of wavelength filters, capturing angle, and illumination sources. There are commercial multispectral capture devices adapted to the specific tasks of the police, but due to their high cost and operation not well adapted to the field work in a crime scene, they are not currently used by forensic units. In our work, we have used a digital SLR camera modified to obtain a nominal sensitivity beyond the visible spectrum. The goal is to obtain forensic evidences from a crime scene using the multispectral camera by an expert in the field knowing which wavelength filters and correct illumination sources should be used, making visible latent evidences hidden from the human-eye. In this paper, we show a procedure to retrieve from latent forensic traces, showing the validity of the system in different real cases (blood stains, hidden/erased tattoos, unlocking patterns on mobile devices). This work opens the possibility of applying multispectral inspections in the forensic field specially for operational units for the location of latent through non-invasive optical procedures.

Fusion of Laser Scans and Image Data—RGB+D for Structural Health Monitoring of Engineering Structures

A novel method for structural health monitoring (SHM) by using RGB+D data has been recently proposed. RGB+D data are created by fusing image and laser scan data, where the D channel represents the distance, interpolated from laser scanner data. RGB channel represents image data obtained by an image sensor integrated in robotic total station (RTS) telescope, or on top of the telescope i.e., image assisted total station (IATS). Images can also be obtained by conventional cameras, or cameras integrated with RTS (different kind of prototypes). RGB+D image combines the advantages of the two measuring methods. Laser scans are used for distance changes in the line of sight and image data are used for displacements determination in two axes perpendicular to the viewing direction of the camera. Image feature detection and matching algorithms detect and match discrete points within RGB+D images obtained from different epochs. These way 3D coordinates of the points can be easily calculated from RGB+D images. In this study, the implementation of this method was proposed for measuring displacements and monitoring the behavior of structural elements under constant load in field conditions. For the precision analysis of the proposed method, displacements obtained from a numerical model in combination with measurements from a high precision linear variable differential transformer (LVDT) sensor was used as a reference for the analysis of determined displacements from RGB+D images. Based on the achieved results, we calculated that in this study, the precision of the image matching and fusion part of the RGB+D is ±1 mm while using the ORB algorithm. The ORB algorithm was determined as the optimal algorithm for this study, with good computing performance, lowest processing times and the highest number of usable features detected. The calculated achievable precision for determining height displacement while monitoring the behavior of structural element wooden beam under different loads is ±2.7 mm.

Designing learning simulations for cognitive absorption

‘The future of learning is immersive. In the future, learning will take the shape of a story, a play, a game; involving multiple platforms and players; driven by dialogue and augmented with technology, an interplay of immersive experiences, data, and highly social virtual worlds’ [1]. Our simulation was designed to raise aspirations as to what is ‘possible’ for our wider faculty as we expand our simulation-based education (SBE). The ‘Godzilla’ multi-casualty exercise offered a fun and engaging theme to the serious focus on student development and assessment. Facilitated at a music venue enabling creative visual and audio backdrops, to a dynamic and immersive learning space. This exploited sensory boundaries in the form of challenging environments, whilst focusing expectations for our student cohorts to demonstrate clinical praxis. The directing staff (DS) included academics, Critical Care Practitioners, Nurses, and Paramedics who ensured a multidisciplinary overview of students’ safety and feedback discussions, appraising decision making, treatment, and management of multiple patient scenarios. Drawing upon the multimedia and interdisciplinary expertise from the faculty, a holistic set of skills brought together the creation of an authentic educational experience, with the evaluation of the students against clinical expectations of a modern healthcare response. The main points of contact were at ‘handover’ post patient extraction and assessment, to senior clinicians. This exercise modelled inclusive approaches, reflected in the seminal Delphi study that identified requirements and opportunities in Immersive Learning namely: Facilitating Authentic Learning Experiences and Developing the Capabilities of the Future Workforce [1]. This approach aligns with the NHS Simulation Strategy [2] but also with the psychological concept of flow and deep absorption in learning proposed by the Open University Innovating Pedagogy report [3]. Premised on the innovation of best learning moments, our student tasks were designed to engender deep involvement through memorable learning activities. 36 level 4 Paramedic students and 24 level 6 Paramedic students undertook the simulated challenges. Facilitators and learners reported high levels of satisfaction and attainment of praxis. Comparisons were recognised between cohorts that informed future adaptations and design, evaluating tasks for future ambitions, fusing interdisciplinary endeavours. This successful exercise met the key learning objectives and students identified this as a ‘memorable’ point in their learning. Inclusion of our allied health professions had handover scenarios filmed with 360° and conventional cameras, and videos were edited for future curricular inclusion. The learning from this inaugural event will inform the diversity and complexity of future tasks set for students. Further feedback capture methods will be used to quantify further investment into future simulation-based educational endeavours. 1. Lee MJ, Georgieva M, Alexander B, Craig E, Richter J. State of XR & Immersive Learning Outlook Report 2021. Immersive Learning Research Network, Walnut, CA. 2021. 2. Health Education England (HEE) A national vision for the role of simulation and immersive technologies in health and care. NHS: 2020 3. Kukulska-Hulme A, Bossu C, Coughlan T, Ferguson R, FitzGerald E, Gaved M, Herodotou C, Rienties B, Sargent J, Scanlon E, Tang J, Wang Q, Whitelock D, Zhang S. Innovating Pedagogy 2021: Open University. Innovation Report 9: 2021

Progress towards a Miniaturised PIV System.

Particle image velocimetry is an important optical flow diagnostic tool due to its capacity for investigating a whole flow field without introducing disturbances. However, a significant drawback of PIV methods is their requirement for optical access, making capturing data in closed cavities and confined spaces extremely challenging. A potential approach to overcome this difficulty is miniaturising the system and placing the optical components inside the model. Conventional cross-correlation PIV methods do not allow this due to the size of current PIV cameras. In this study, a miniaturised autocorrelation-based stereo PIV system, which is volumetrically 1.2% of the conventional PIV cameras, was developed and tested. The miniature system is compared with a conventional stereo PIV in wind tunnel experiments up to 16 m/s free stream velocity and a 1.6% velocity difference is observed in the boundary layer flow. Despite a comparatively slow measurement rate of 4.5 Hz, the miniature PIV system demonstrates the ability to measure inside confined spaces and cavities and the ability to be mounted on board models and vehicles. However, limitations remain around conducting measurements with large velocity ranges and with regions of reversed flow due to the challenge of resolving a velocity of 0 m/s.

Joint Framework for Single Image Reconstruction and Super-Resolution With an Event Camera.

Event cameras sense brightness changes in each pixel and yield asynchronous event streams instead of producing intensity images. They have distinct advantages over conventional cameras, such as a high dynamic range (HDR) and no motion blur. To take advantage of event cameras with existing image-based algorithms, a few methods have been proposed to reconstruct images from event streams. However, the output images have a low resolution (LR) and are unrealistic. Low-quality outputs stem from broader applications of event cameras, where high-quality and high-resolution (HR) images are needed. In this work, we consider the problem of reconstructing and super-resolving images from LR events when no ground truth (GT) HR images and degradation models are available. We propose a novel end-to-end joint framework for single image reconstruction and super-resolution from LR event data. Our method is primarily unsupervised to handle the absence of real inputs from GT and deploys adversarial learning. To train our framework, we constructed an open dataset, including simulated events and real-world images. The use of the dataset boosts the network performance, and the network architectures and various loss functions in each phase help improve the quality of the resulting image. Various experiments showed that our method surpasses the state-of-the-art LR image reconstruction methods for real-world and synthetic datasets. The experiments for super-resolution (SR) image reconstruction also substantiate the effectiveness of the proposed method. We further extended our method to more challenging problems of HDR, sharp image reconstruction, and color events. In addition, we demonstrate that the reconstruction and super-resolution results serve as intermediate representations of events for high-level tasks, such as semantic segmentation, object recognition, and detection. We further examined how events affect the outputs of the three phases and analyze our method's efficacy through an ablation study.

Rapid Photogrammetry with a 360-Degree Camera for Tunnel Mapping

Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry is a viable method to digitize underground spaces for inspection, documentation, or remote mapping. However, the conventional image acquisition process can be laborious and time-consuming. Previous studies confirmed that the acquisition time can be reduced when using a 360-degree camera to capture the images. This paper demonstrates a method for rapid photogrammetric reconstruction of tunnels using a 360-degree camera. The method is demonstrated in a field test executed in a tunnel section of the Underground Research Laboratory of Aalto University in Espoo, Finland. A 10 m-long tunnel section with exposed rock was photographed using the 360-degree camera from 27 locations and a 3D model was reconstructed using SfM-MVS photogrammetry. The resulting model was then compared with a reference laser scan and a more conventional digital single-lens reflex (DSLR) camera-based model. Image acquisition with a 360-degree camera was 3× faster than with a conventional DSLR camera and the workflow was easier and less prone to errors. The 360-degree camera-based model achieved a 0.0046 m distance accuracy error compared to the reference laser scan. In addition, the orientation of discontinuities was measured remotely from the 3D model and the digitally obtained values matched the manual compass measurements of the sub-vertical fracture sets, with an average error of 2–5°.

Perspective Transformer and MobileNets-Based 3D Lane Detection from Single 2D Image

Three-dimensional (3D) lane detection is widely used in image understanding, image analysis, 3D scene reconstruction, and autonomous driving. Recently, various methods for 3D lane detection from single two-dimensional (2D) images have been proposed to address inaccurate lane layouts in scenarios (e.g., uphill, downhill, and bumps). Many previous studies struggled in solving complex cases involving realistic datasets. In addition, these methods have low accuracy and high computational resource requirements. To solve these problems, we put forward a high-quality method to predict 3D lanes from a single 2D image captured by conventional cameras, which is also cost effective. The proposed method comprises the following three stages. First, a MobileNet model that requires low computational resources was employed to generate multiscale front-view features from a single RGB image. Then, a perspective transformer calculated bird’s eye view (BEV) features from the front-view features. Finally, two convolutional neural networks were used for predicting the 2D and 3D coordinates and respective lane types. The results of the high-reliability experiments verified that our method achieves fast convergence and provides high-quality 3D lanes from single 2D images. Moreover, the proposed method requires no exceptional computational resources, thereby reducing its implementation costs.

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors.

Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.

PiRamid: A compact Raspberry Pi imaging box to automate small-scale time-lapse digital analysis, suitable for laboratory and field use

Digital imaging permits the quantitation of many experiments, such as microbiological growth assays, but laboratory digital imaging systems can be expensive and too specialised. The Raspberry Pi camera platform makes automated, controlled imaging affordable with accessible customisation. When combined with open source software and open-source 3D printed hardware, the control over image quality and capture of this platform permits the rapid development of novel instrumentation. Here we present “PiRamid”, a compact, portable, and inexpensive enclosure for autonomous imaging both in the laboratory and in the field. The modular three-piece 3D printed design makes it easy to incorporate different camera systems or lighting configurations (e.g., single wavelength LED for fluorescence). The enclosed design allows complete control of illumination unlike a conventional digital camera or smartphone, on a tripod or handheld, under ambient lighting. The stackable design permits rapid sample addition or camera focus adjustment, with a corresponding change in magnification and resolution. The entire unit is small enough to fit within a microbiological incubator, and cheap enough (∼£100) to scale out for larger parallel experiments. Simply, Python scripts fully automate illumination and image capture for small-scale experiments with an ∼110×85 mm area at 70–90 µm resolution. We demonstrate the versatility of PiRamid by capturing time-resolved, quantitative image data for a wide range of assays. Bacterial growth kinetics was captured for conventional microbiology (agar Petri dishes), 3D printed custom microbiology labware and microfluidic microbiology. To illustrate application beyond microbiology, we demonstrate time-lapse imaging of crystal growth and degradation of salad leaves. Minor modifications permit epi-illumination by addition of a LED ring to the camera module. We conclude that PiRamid permits inexpensive digital capture and quantitation of a wide range of experiments by time-lapse imaging to simplify both laboratory and field imaging.

E2SRI: Learning to Super-Resolve Intensity Images From Events

An event camera reports per-pixel intensity differences as an asynchronous stream of events with low latency, high dynamic range (HDR), and low power consumption. This stream of sparse/dense events limits the direct use of well-known computer vision applications for event cameras. Further applications of event streams to vision tasks that are sensitive to image quality issues, such as spatial resolution and blur, e.g., object detection, would benefit from a higher resolution of image reconstruction. Moreover, despite the recent advances in spatial resolution in event camera hardware, the majority of commercially available event cameras still have relatively low spatial resolutions when compared to conventional cameras. We propose an end-to-end recurrent network to reconstruct high-resolution, HDR, and temporally consistent grayscale or color frames directly from the event stream, and extend it to generate temporally consistent videos. We evaluate our algorithm on real-world and simulated sequences and verify that it reconstructs fine details of the scene, outperforming previous methods in quantitative quality measures. We further investigate how to (1) incorporate active pixel sensor frames (produced by an event camera) and events together in a complementary setting and (2) reconstruct images iteratively to create an even higher quality and resolution in the images.

Recognition of arable soils from photographs obtained as part of crowdsourcing technologies

The study focuses on the possibilities of using photographs obtained using crowdsourcing technologies for the operational inventory of arable soils. The object of the study is the spectral reflectance of the open surface of arable soils of the test plots, measured using a HandHeld-2 spectroradiometer operating in the range of 325–1 075 nm, and their image in photographs taken with conventional cameras. Test sites are located in the Tula, Moscow and Tver regions. The soils of the test plots are sod-podzolic, gray forest, and leached chernozems. Based on the analysis of photographs of the surface and information obtained using a spectroradiometer, a set of spectral parameters in the RGB, YMC and HSI color systems, as well as their ratios (45 parameters), was calculated. These parameters were used to separate the analyzed soil types using classification trees. The accuracy of classification based on the results of validation varies from 63–100%. At the same time, the parameters of the HSI and YMC color systems turned out to be more informative than the parameters of the RGB color system. The established classification rules can later be used to determine the classification position of soils from images collected using crowdsourcing technologies.

Automatic Assessment of the Type and Intensity of Agitated Hand Movements

With increasing numbers of people living with dementia, there is growing interest in the automatic monitoring of agitation. Current assessments rely on carer observations within a framework of behavioural scales. Automatic monitoring of agitation can supplement existing assessments, providing carers and clinicians with a greater understanding of the causes and extent of agitation. Despite agitation frequently manifesting in repetitive hand movements, the automatic assessment of repetitive hand movements remains a sparsely researched field. Monitoring hand movements is problematic due to the subtle differences between different types of hand movements and variations in how they can be carried out; the lack of training data creates additional challenges. This paper proposes a novel approach to assess the type and intensity of repetitive hand movements using skeletal model data derived from video. We introduce a video-based dataset of five repetitive hand movements symptomatic of agitation. Using skeletal keypoint locations extracted from video, we demonstrate a system to recognise repetitive hand movements using discriminative poses. By first learning characteristics of the movement, our system can accurately identify changes in the intensity of repetitive movements. Wide inter-subject variation in agitated behaviours suggests the benefit of personalising the recognition model with some end-user information. Our results suggest that data captured using a single conventional RGB video camera can be used to automatically monitor agitated hand movements of sedentary patients.

Electrically addressed focal stack plenoptic camera based on a liquid-crystal microlens array for all-in-focus imaging.

Focal stack cameras are capable of capturing a stack of images focused at different spatial distance, which can be further integrated to present a depth of field (DoF) effect beyond the range restriction of conventional camera's optics. To date, all of the proposed focal stack cameras are essentially 2D imaging architecture to shape 2D focal stacks with several selected focal lengths corresponding to limited objective distance range. In this paper, a new type of electrically addressed focal stack plenoptic camera (EAFSPC) based on a functional liquid-crystal microlens array for all-in-focus imaging is proposed. As a 3D focal stack camera, a sequence of raw light-field images can be rapidly manipulated through rapidly shaping a 3D focal stack. The electrically addressed focal stack strategy relies on the electric tuning of the focal length of the liquid-crystal microlens array by efficiently selecting or adjusting or jumping the signal voltage applied over the microlenses. An algorithm based on the Laplacian operator is utilized to composite the electrically addressed focal stack leading to raw light-field images with an extended DoF and then the all-in-focus refocused images. The proposed strategy does not require any macroscopic movement of the optical apparatus, so as to thoroughly avoid the registration of different image sequence. Experiments demonstrate that the DoF of the refocused images can be significantly extended into the entire tomography depth of the EAFSPC, which means a significant step for an all-in-focus imaging based on the electrically controlled 3D focal stack. Moreover, the proposed approach also establishes a high correlation between the voltage signal and the depth of in-focus plane, so as to construct a technical basis for a new type of 3D light-field imaging with an obvious intelligent feature.

Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (Sψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the Sψ. The results showed that the information in the visible domain was highly related to the Sψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard.

Smart Traffic Data for the Analysis of Sustainable Travel Modes

We present and validate the image analysis algorithm μ-scope to capture personal mobility devices’ (PMDs) movement characteristics and extract their movement dynamics even when they interact with each other and with pedestrians. Experimental data were used for validation of the proposed algorithm. Data were collected through a large-scale, semicontrolled, real-track experiment at the University of Patras campus. Participants (N = 112) included pedestrians, cyclists, and e-scooter drivers. The experiment was video recorded, and μ-scope was used for trajectory extraction. Some of the participants had installed, beforehand, the Phyphox application in their smartphones. Phyphox accurately measures x-y-z acceleration rates and was used, in our case, as the baseline measurement (i.e., “ground truth”). Statistical comparison between Phyphox and camera-based measurements shows very low difference in most cases. High pedestrian densities were the only case where relatively high root mean square errors were registered. The proposed algorithm can be thus considered capable of producing reliable speed and acceleration estimates. Low-quality conventional smartphone cameras were used in this experiment. As a result, the proposed method can be easily applied to all urban contexts under normal traffic conditions, but eventually not in the case of special or emergency events generating very high pedestrian densities.

Camera Animation for Immersive Light Field Imaging

Among novel capture and visualization technologies, light field has made significant progress in the current decade, bringing closer its emergence in everyday use cases. Unlike many other forms of 3D displays and devices, light field visualization does not depend on any viewing equipment. Regarding its potential use cases, light field is applicable to both cinematic and interactive contents. Such contents often rely on camera animation, which is a frequent tool for the creation and presentation of 2D contents. However, while common 3D camera animation is often rather straightforward, light field visualization has certain constraints that must be considered before implementing any variation of such techniques. In this paper, we introduce our work on camera animation for light field visualization. Different types of conventional camera animation were applied to light field contents, which produced an interactive simulation. The simulation was visualized and assessed on a real light field display, the results of which are presented and discussed in this paper. Additionally, we tested different forms of realistic physical camera motion in our study, and based on our findings, we propose multiple metrics for the quality evaluation of light field visualization in the investigated context and for the assessment of plausibility.

Assessing the subjective quality of smartphone anterior segment photography: a non-inferiority study

PurposeTo assess the subjective quality of anterior segment photos taken from a smartphone camera adapted to the slit lamp compared to a commercial inbuilt slit-lamp camera.MethodsNon-inferiority study. Five paired images of the anterior segment of normal eyes were taken using an iPhone 11 (Apple, Inc., Calif., USA) camera attached to a universal slit-lamp adaptor and a commercial inbuilt slit-lamp camera (Haag-Streit Diagnostics, Bern, Switzerland). Images were collated into a survey in which ophthalmology students, residents, registrars, and consultants participated to select the image taken from the inbuilt slit-lamp camera. If the image quality was subjectively indistinguishable, we expected a 50:50 split for each photograph that was presented. We selected a 10% non-inferiority margin, with the hypothesis that no less than 40% of images believed to be from the conventional camera were in fact from the smartphone camera.ResultsThere were 27 respondents in the survey: ophthalmology consultants (n = 7), registrars (n = 10), residents (n = 7), intern (n = 1) and students (n = 2). The mean correct identification across the respondents was 11.3 out of 25 (45.2%) images. Overall, the smartphone camera was non-inferior to the inbuilt slit-lamp camera (p < 0.001). The non-inferiority of the smartphone camera was significant for consultants (47.4%, p < 0.01), registrars (47.6%, p < 0.001) and residents (37.7%, p < 0.0001).ConclusionsAnterior segment images obtained with a smartphone camera were non-inferior to the commercial inbuilt slit-lamp camera. Smartphone cameras may be a non-inferior tool for communication of anterior segment images having implications for the ease of access to quality telehealth consultations.

Light field extraction from a conventional camera

This paper presents a single-shot light field (LF) acquisition method using a conventional camera. The proposed method does not require any modification in the camera design and instead involves an offline calibration process. The calibration step involves the estimation of the point spread function (PSF) for different perspectives at all sampled depths. PSF relates perspective image captured with a narrow aperture opening to the central image captured with wider aperture opening containing multiple perspectives of the same scene superimposed on each other. During LF extraction, different perspective images are obtained by deconvolving the wide aperture input image with the set of PSFs estimated in the offline calibration phase. The experimental results demonstrate that the proposed method generates high angular and spatial resolution LF.