Conventional Digital Camera Research Articles

Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as smart dust. Several efforts focused primarily on computing and networking capabilities, but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data were processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

A systematic scoping review protocol to summarise and appraise the use of artificial intelligence in the analysis of digital videos of invasive general surgical procedures.

Intraoperative video recordings are a valuable addition to operative written documentation. However, the review of these videos often requires surgical expertise and takes considerable time. While a large amount of work has been undertaken to understand the role of artificial intelligence (AI) in healthcare more generally, the application of these techniques to automate the analysis of surgical videos is currently unclear. In this systematic scoping review, we sought to give a contemporary overview of the use of AI research in the analysis of digital videos of invasive general surgical procedures. We will describe and summarise the study characteristics, purpose of the applications and stage of development, to ascertain how these techniques might be applied in future research and to identify gaps in current knowledge (e.g. uncertainties about the study methods). Systematic searches will be conducted in OVID Medline and Embase, using terms related to 'artificial intelligence', 'surgery' and 'video' to identify all potentially relevant studies published since 1st January 2012. All primary studies where AI has been applied to the analysis of videos (recorded by conventional digital cameras or laparoscopic or robotic-assisted technology) of general surgical procedures will be included. Data extraction will include study characteristics, governance, details of video datasets and AI models, measures of accuracy, validation and any reported limitations. No ethical approval is required as primary data will not be collected. The results will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal.

Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in image classification, and developing soil property prediction techniques using this algorithm will be extremely beneficial to soil management. We present the convolution neural network models for estimating water content and dry density using soil surface images. Soil surface images were taken with a conventional digital camera. The range of water content and dry density were determined considering general upland soil conditions. Each image was divided into segmented images and used for model training and validation. The developed model confirmed that the model can learn soil features through appropriate image argumentation of few of original soil surface images. Additionally, it was possible to predict the soil water content in a situation where various soil dry density conditions were considered.

A joint validation study on camera-aided illuminance measurement

This joint study conducted in the United States and Japan reviewed existing illuminance measurement methods that use a conventional digital single-lens reflex camera, including (a) per-pixel illuminance calculation using non-fisheye wide-angle lenses, (b) hemispherical projection-based illuminance calculation, (c) the KU method developed by the University of Kansas (KU) and (d) the L-CEPT method developed by the Japanese team for per-pixel illuminance calculation using 180° fisheye lens. Next, laboratory and field measurements were conducted in 32 test scenarios in Kansas and Japan to improve and validate the two similar methods (c) and (d) for per-pixel illuminance calculation and compare them to the simplified method (b) using orthographic projection-based illuminance calculation. Based on the test results, the camera-aided illuminance measurement method could have an error rate of 13.7% ± 15.3% (SD) in all 32 scenarios. Large errors (>25%) were observed in six extremely dynamic gymnasiums with rapidly changing morning sunlight. Alternatively, an average error rate of 7.7% ± 7.5% was observed in the 26 test scenarios without rapidly changing direct sunlight. The simplified orthographic projection-based method could achieve the same level of accuracy as the per-pixel KU/L-CEPT method. Yet, multiple issues on camera-aided illuminance measurement still need further investigation.

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.

A novel image dataset for source camera identification and image based recognition systems

Multimodal emotion recognition has attracted a great deal of attention in recent years, with new interesting applications now being considered. One promising application is in the digital image forensics fields where, for example, it gives the possibility to automatically highlight subjects that are in pain, in digital images under examination, by analyzing their facial expressions. However, finding an image that represents a possible crime leaves the problem of identifying the device used to take the image open. Such a problem has been addressed by Source Camera Identification algorithms (SCI, for short). These algorithms analyze some features hidden in a target image to find traces left by the sensor that captured the image. A particularly challenging case is when the candidate source cameras for an image under investigation are of the same manufacturer and model. A fair and universal assessment of these algorithms is only possible if standard datasets are used for their benchmarking. However, our comprehensive analysis has shown that the majority of the datasets proposed so far contain a collection of images taken with different types of cameras, mostly smartphones. We fill this gap by presenting UNISA2020, a novel image dataset that contains a large collection of real-world images taken with multiple conventional digital cameras of the same type. The images in our dataset have been assembled so as to avoid artifacts that could negatively affect the identification process. To validate our dataset, we also performed a comparative experimental analysis to investigate the performance of an SCI reference algorithm when running on our dataset as well as on other SCI standard datasets.

A Multicultivar Approach for Grape Bunch Weight Estimation Using Image Analysis

The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach. A set of 192 bunches from four cultivars, collected at sites located in Portugal and South Africa, were imaged using a conventional digital RGB camera, followed by image analysis, where several bunch features were extracted, along with physical measurements performed in laboratory conditions. Image data features were explored as predictors of bunch weight, individually and in a multiple stepwise regression analysis, which were then tested on 37% of the data. The results show that the variables bunch area and visible berries are good predictors of bunch weight (R2 ranging from 0.72 to 0.90); however, the simple regression lines fitted between these predictors and the response variable presented significantly different slopes among cultivars, indicating cultivar dependency. The elected multiple regression model used a combination of four variables: bunch area, bunch perimeter, visible berry number, and average berry area. The regression analysis between the actual and estimated bunch weight yielded a R2 = 0.91 on the test set. Our results are an important step towards automatic yield estimation in the vineyard, as they increase the possibility of applying image-based approaches using a generalized model, independent of the cultivar.

A SIMPLE DEVICE FOR MONITORING CRACKS FROM PHOTOGRAPHS

Abstract. The article presents a simple technological solution PMC (PhotoMicrometer Contrast) for monitoring cracks and deformation joints in buildings and structures. The solution is based on the methods of image processing and photogrammetry. The monitoring equipment includes a special marker, a digital camera and specialized processing software. The marker consists of two thin plastic plates where Aruco marks and special measuring areas are printed. There are two types of markers – for monitoring in one or two directions of crack development. While monitoring is performed, two plates are fixed on both sides of the crack or deformation joint. Then the plates are photographed with a digital camera as often as the observation cycle requires. Any conventional modern digital camera is suitable for photographing after successfully passing the distortion effect test described in the article. Image processing in the software includes automatic identification of the marker in the image, binarization of the image, determination of its scale and dimensions of measuring areas in the current observation cycle. For processing images made at large angles of inclination and rotation, a preliminary solution of the resection in space and the images rectification to the found angles is provided. The dynamics of crack development is determined by the results of several cycles of observations collected over period. Our technology ensures the accuracy of the determination of coordinates and deformations at the level of 0.05 mm and higher for photographing distances from 0.3 to 50 m.

Reconstructing Clear Image for High-Speed Motion Scene With a Retina-Inspired Spike Camera

Conventional digital cameras typically accumulate all the photons within an exposure period to form a snapshot image. It requires the scene to be quite still during the imaging time, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scenes. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic light intensity variation continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, which fires a spike and restarts the photon accumulation immediately once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from captured spike stream, this paper presents an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via adaptive temporal filtering. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with low model complexity. Experimental results demonstrate that the proposed scheme outperforms the existing schemes significantly, producing much better objective and subjective qualities for spike camera image reconstruction.

Selfie fundus imaging for diabetic retinopathy screening.

BackgroundRegular screening for retinopathy and timely intervention reduces blindness from diabetes by 90%. Screening is currently dependent on the interpretation of images captured by trained technicians. Inherent barriers of accessibility and affordability with this approach impede widespread success of retinopathy screening programs. Herein, we report our observations on the potential of a novel approach, Selfie Fundus Imaging (SFI), to enhance diabetic retinopathy screening.MethodsThe study was undertaken over a two-month period during COVID 19 lockdown. 60 diabetic patients participated in the study. Retinal images were captured using three different approaches, handheld smartphone-based photographs captured by patients themselves after a short video-assisted training session (SFI group), and smartphone-based photographs captured by a trained technician and photographs taken on desktop conventional digital fundus camera (Gold standard). Sensitivity and kappa statistics was determined for retinopathy and macular oedema grading.FindingsMean age of the study participants was 52.4 years ± 9.8 years and 78% were men. Of 120 images captured using SFI, 90% were centred-gradable, 8% were decentred-gradable and 2% were ungradable. 82% patients captured the image within a minute (majority by 31–45 s). The sensitivity of SFI to detect diabetic retinopathy was 88.39%. Agreement between SFI grading and standard fundus photograph grading was 85.86% with substantial kappa (0.77). For the detection of diabetic macular oedema, the agreement between SFI images and standard images was 93.67, with almost perfect kappa (0.91).ConclusionFundus images were captured by patients using SFI without major difficulty and were comparable to images taken by trained specialist. With greater penetrance, advances, and availability of mobile photographic technology, we believe that SFI would positively impact the success of diabetic retinopathy screening programs by breaking the barriers of availability, accessibility, and affordability. SFI could ensure continuation of screening schedules for diabetic retinopathy, even in the face a highly contagious pandemic.

MODERN POSSIBILITIES OF DOCUMENTATION AND REPLICATION OF ARCHAEOLOGICAL FINDS

Abstract. This contribution deals with the documentation of archaeological finds using close-range photogrammetry. The method of close-range photogrammetry is presented in the form of modern technology SfM (Structure from Motion), which has become very popular in the last ten years and has infiltrated a number of fields other than geodesy. Archaeological excavations and documentation of finds were carried out as part of the Iraqi / Kurdistan expedition in 2018. Photogrammetry was used experimentally to create 3D models using a conventional digital camera. The results are satisfactory, and it was possible to create copies of the findings using rapid prototyping technology (3D printing). However, processing is not entirely simple and creating a quality model is time consuming.

Bionic Birdlike Imaging Using a Multi-Hyperuniform LED Array.

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.

Learning digital camera pipeline for extreme low-light imaging

In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR). We present a data-driven approach that learns the desired properties of well-exposed images and reflects them in images that are captured in extremely low ambient light environments, thereby significantly improving the visual quality of these low-light images. The recent works on this problem only consider a pixel-level loss metric that ignores perceptual quality and thus generate outputs susceptible to visual artifacts. To address this problem, we propose a new loss function that exploits the characteristics of both pixel-wise and perceptual metrics, enabling our deep neural network to learn the camera processing pipeline to transform the short-exposure, low-light RAW sensor data to well-exposed sRGB images. The results show that our method outperforms the state-of-the-art according to psychophysical tests as well as pixel-wise standard metrics and recent learning-based perceptual image quality measures. In essence, the proposed model can potentially replace the conventional digital camera pipeline for the specific case of extreme low-light imaging.

Illumination Calibration for Computational Ghost Imaging

We propose a fast calibration method to compensate the non-uniform illumination in computational ghost imaging. Inspired by a similar procedure to calibrate pixel response differences for detector arrays in conventional digital cameras, the proposed method acquires one image of an all-white paper to determine the non-uniformity of the illumination, and uses the information to calibrate any further reconstructed images under the same illumination. The numerical and experimental results are in a good agreement, and the experimental results showed that the root mean square error of the reconstructed image was reduced by 79.94% after the calibration.

Selfie Fundus Imaging (SFI) for Diabetic Retinopathy Screening During COVID 19 Pandemic

Background: Regular screening for retinopathy and timely intervention reduces blindness from diabetes by 90%. Screening is currently dependent on interpretation of images captured by trained technicians. Inherent barriers of accessibility and affordability with this approach impedes widespread success of retinopathy screening programs. Herein, we report our observations on the potential of a novel approach, Selfie Fundus Imaging (SFI), to enhance retinopathy screening.Methods: The study was undertaken over a two-month period during COVID 19 lockdown. 60 diabetic patients participated in the study. Retinal images were captured using three different approaches, handheld smartphone based photographs captured by patients themselves after a short video assisted training session (SFI group), smartphone based photographs captured by a trained technician and photographs taken on desktop conventional digital fundus camera (Gold standard). Sensitivity and kappa statistics was determined for retinopathy and macular edema grading.Findings: Mean age of the study participants was 52.4 years +/- 9.8 years and 78% were men. Of 120 images captured using SFI, 90% were centred-gradable, 8% were decentred-gradable and 2% were ungradable. 82% patients captured the image within a minute (majority by 31-45 seconds). The sensitivity of SFI to detect diabetic retinopathy was 88.39%. Agreement between SFI grading and standard fundus photograph grading was 85.86% with substantial kappa (0.77). For the detection of diabetic macular edema, the agreement between SFI images and standard images was 93.67, with almost perfect kappa (0.91).Interpretation: Fundus images were captured by patients using SFI without major difficulty and were comparable to images taken by trained specialist. With greater penetrance, advances, and availability of mobile photographic technology, we believe that SFI would positively impact success of diabetic retinopathy screening programs by breaking the barriers of availability, accessibility, and affordability. SFI could ensure continuation of screening schedules for diabetic retinopathy, even in the face a highly contagious pandemic.Funding: The study was not funded by any intramural or extramural agencies/ companies/ sponsors. It was carried out independently by the authors without seeking any funding.Conflict of Interest: None to declare. Ethical Approval: This prospective, comparative study was initiated after obtaining due ethical clearance (IECPG-646/19/12/2018) from the institutional ethics committee and followed the tenets of the Helsinki Declaration.

Photon-triggered self-powered all electronics with graphene-silicon hybrid device

Breaking the electron-hole occupancy symmetry in graphene is a powerful approach for engineering novel optoelectronic phenomena. Traditionally, this has been achieved by electrostatic gating. However, the realization of such phenomena by using photons, without any external power supply, has remained a challenge. In this study, we developed photon-triggered, self-biased, graphene-based diverse electronic circuits, including lateral p–n and n–p homojunctions, switches, transistors, and logic gates (NOT, OR, and AND), which were further supported by local probe measurements. Moreover, the proposed device operates in a wide spectral range (from ultraviolet-visible-near-infrared range) under the self-biased condition (0 V) with fast temporal processing (~12 μs) even at the nanoscale (<50 nm). As immediate applications, we demonstrated high-speed (~72 km/h), event-based neuromorphic sensing. This work offers unprecedented opportunities on a single platform in the fields of digital/analog electronics and optoelectronics with nanoscale control and provides a neuromorphic solution to the issues encountered in conventional digital cameras.

Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Study of possibilities for light marker coordinate measuring with light field digital cameras

The paper considers the possibilities of recording the laser emitter light markers on the surface of the object under study by an optoelectronic system based on digital light field cameras. The technique of experimental research of coordinates and parameters of light markers is described. The mathematical model of light markers is presented. Algorithms have been developed for determining the coordinates of light markers on the surface of controlled products. The accuracy of determining the coordinates and dimensions of light markers is analyzed. The necessity for high-precision measuring systems to perform preliminary calibration of laser emitters according to the proposed method, taking into account the design range of light markers, is shown. It was found that the accuracy of determining the coordinates of light markers for design surfaces with digital cameras of the light field is several times higher than with conventional digital cameras.

Determination of a Kinetic Law of Phosphorescence Decay Using a Conventional Photo Camera and Free Image Processing Software

Investigation of the kinetics of phosphorescence decay is a suitable learning task in studying the formal kinetics, and also properties of crystal phosphors. Kinetics of phosphorescence decay of crystal phosphors can be experimentally investigated at home as part of distance learning, using a conventional digital photo camera and a luminescent object such as a glow-in-the-dark toy. During the work, students get acquainted with the basic laws of phosphorescence decay kinetics and determine which law is observed in their object and what mechanism it corresponds to.

New application of hyperspectral imaging to steady-state plasma observations.

A new application of hyperspectral imaging (HSI) to steady-state plasma emission observations is proposed because of its prominent feature: an HSI camera records a two-dimensional image, and each spatial pixel contains spectral data typically with more than a hundred bands, while conventional digital cameras have only three bands. The characterization of an HSI camera (Specim IQ) has been performed during steady-state plasma-material interaction experiments using the linear plasma device PISCES-A. By easily subtracting the background/continuum emission in contrast to conventional filter cameras, two-dimensional images of multiple emission lines at different wavelengths are simultaneously obtained during a single measurement, demonstrating the advantage in plasma emission observations.