Traditional Digital Cameras Research Articles

Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

Drone testing for 3D reconstruction of massive mounted skeletons in museums: the case of Mammuthus meridionalis (Nesti 1825) from Madonna della Strada (Scoppito, L’Aquila, Italy)

ABSTRACT Unmanned aerial vehicles (UAVs), have seen tremendous development in the last decade, with numerous applications in civil and research fields. Drones’ success, particularly in the field of research, is due to a number of factors, including rapid technological advancement, tool versatility, and prices that are becoming increasingly affordable even for small research groups or individuals. Given the versatility and ongoing development of micro drones, we tested the use of micro-drones in vertebrate palaeontology to reconstruct mounted skeletons using the photogrammetry method. The experiment was carried out on a massive specimen of Mammuthus meridionalis (Nesti 1825) from Madonna della Strada, which is on display at the east bastion of the Spanish Fortress in L’Aquila (Abruzzo, Central Italy), comparing the results with those obtained using the traditional method using a digital camera. Even though both the traditional digital camera and the drone methods produced a high-resolution 3D model of the skeleton, the results obtained, indeed, lead us to consider the use of micro-drones in museum structures as a very interesting and promising new field of application. Drones provide a simple, fast, and non-invasive system for the study, monitoring, and enhancement of cultural heritage in all of its possible manifestations.

A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

The accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

CONSTRUCTION OF OPTICAL CONTROL ALGORITHMS FOR SMALL-SIZE DISTANT LIGHT OBJECTS

A mathematical model of the brightness structure of an ensemble of small-sized distant light objects in the plane of a photodetector is presented. The structure and principles of construction of optoelectronic systems for registration of distant and small-sized light objects are considered. A method for constructing of algorithms for determining the parameters of an ensemble of objects of different distances, obtained by an optoelectronic system with a digital camera of the light field, taking into account its characteristic features, is developed. A special experimental stand for the study of objects in distant conditions based on a telescopic system and a digital registrator is presented. The principal possibility of creating a light field correlator is shown. A number of algorithms and functions are described, as well as their capabilities to accurately measure the coordinates and parameters of small-sized light objects. The advantages and disadvantages of algorithms based on the continuous wavelet transform are investigated. A comparative analysis of the accuracy of determining the coordinates, dimensions, and features of small-sized objects by various algorithms is presented. It is shown, that the accuracy of determining the coordinates and parameters of small-sized remote objects by digital cameras of the light field is several times higher than by traditional digital cameras.

Color conversion matrices in digital cameras: a tutorial

Color conversion matrices and chromatic adaptation transforms (CATs) are of central importance when converting a scene captured by a digital camera in the camera raw space into a color image suitable for display using an output-referred color space. In this article, the nature of a typical camera raw space is investigated, including its gamut and reference white. Various color conversion strategies that are used in practice are subsequently derived and examined. The strategy used by internal image-processing engines of traditional digital cameras is shown to be based upon color rotation matrices accompanied by raw channel multipliers, in contrast to the approach used by smartphones and commercial raw converters, which is typically based upon characterization matrices accompanied by conventional CATs. Several advantages of the approach used by traditional digital cameras are discussed. The connections with the color conversion methods of the DCRaw open-source raw converter and the Adobe digital negative converter are also examined, along with the nature of the Adobe color and forward matrices.

Circumscribed choroidal haemangioma: Indocyanine green angiography features on scanning laser ophthalmoscopy versus traditional digital fundus photography.

Circumscribed choroidal haemangioma (CCH) has several characteristic clinical and angiographic features. We aimed to compare indocyanine green angiography (ICG) findings of CCH captured on a traditional digital camera system (DCS) to newer scanning laser ophthalmoscopy (SLO) platforms. A total of 35 patients over a 10-year period diagnosed with CCH using ICG were included (18 imaged with DCS and 17 with SLO). On early ICG frames, intrinsic vessels were apparent in two-thirds (12/18; 67%) of the DCS group compared with all of eyes in the SLO group (p = 0.020). In addition, at maximal hyperfluorescence, most eyes imaged with DCS had a feathery appearance (16/18; 89%) compared with those in the SLO group which all (17/17; 100%) displayed a granular appearance (p < 0.001). The presence of hot spots at maximal hyperfluorescence was also more common in the SLO group (12/17; 71%) versus the DCS group (0/18; 0%) (p < 0.001). Finally, intrinsic vessels and vascular loops could be identified throughout the entire duration of the ICG in 100% of the SLO cases (17/17) versus only 11% (2/18) of DCS cases (p < 0.001). The visualization of intrinsic vessels, vascular loops, and "hot spots" in CCH is significantly enhanced with SLO compared with DCS. Many characteristic mid-late angiographic findings of CCH are more optimally visualized on SLO which may negate the need for late frames (>30 min) without compromising diagnostic accuracy.

Forensic source identification using JPEG image headers: The case of smartphones

A common problem in forensic investigations is the identification of the source of multimedia data, i.e., determining the model, make or individual device that recorded media content. In contrast to methods based on sensor noise, source linkage based on header information of media items allows for easy automation. Such header information involves metadata like EXIF tags and the parameterization of the JPEG algorithm. While traditional digital cameras typically had a fixed software stack that makes it straightforward to fingerprint a device, modern mobile devices vary considerably in their software stack over time. We perform a large-scale study of JPEG header information from Apple smartphones to investigate the effect of this development on the possibility to perform source identification. Our analysis shows that identification of the concrete hardware is much harder for smartphones than it is for traditional cameras. However, identification of software stack, particularly the operating system version and selected apps, is well feasible.

A Bayesian Nonparametric Model for Temperature-Emissivity Separation of Long-Wave Hyperspectral Images

Long-wave infrared (LWIR) hyperspectral imagers (HSI) image a scene by collecting high-resolution spectra at each pixel. The data are similar to a camera image but have a large number of narrow spectral bands rather than the familiar broad three bands of red-green-blue in a traditional digital camera. Materials in LWIR are emissive (rather than reflective) and have unique spectra that can be used for material detection and identification. The measured spectra are a convolution of the material spectra (emissivity), the black body temperature (Planck curve), other interacting environmental spectral sources, and measurement error. One approach to material identification is temperature-emissivity separation (TES), which separates or deconvolves the material spectra from the temperature curve. To accomplish this task, we develop a unique flexible model which combines the mathematical model of the physical processes within a Bayesian nonparametric framework. In addition to offering interpretable estimates of model parameters, this model is able to identify material emissivity spectra and cluster pixels into appropriate material groups. We demonstrate our method using both a synthetic and measured dataset. The online supplementary material contains an appendix of the details of the sampling algorithm.

Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today's digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics. In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor. In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations. Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.

Quantitative Light Fluorescence (QLF) and Polarized White Light (PWL) assessments of dental fluorosis in an epidemiological setting

BackgroundTo determine if a novel dual camera imaging system employing both polarized white light (PWL) and quantitative light induced fluorescence imaging (QLF) is appropriate for measuring enamel fluorosis in an epidemiological setting. The use of remote and objective scoring systems is of importance in fluorosis assessments due to the potential risk of examiner bias using clinical methods.MethodsSubjects were recruited from a panel previously characterized for fluorosis and caries to ensure a range of fluorosis presentation. A total of 164 children, aged 11 years (±1.3) participated following consent. Each child was examined using the novel imaging system, a traditional digital SLR camera, and clinically using the Dean’s and Thylstrup and Fejerskov (TF) Indices on the upper central and lateral incisors. Polarized white light and SLR images were scored for both Dean’s and TF indices by raters and fluorescence images were automatically scored using software.ResultsData from 164 children were available with a good distribution of fluorosis severity. The automated software analysis of QLF images demonstrated significant correlations with the clinical examinations for both Dean’s and TF index. Agreement (measured by weighted Kappa’s) between examiners scoring clinically, from polarized photographs and from SLR images ranged from 0.56 to 0.92.ConclusionsThe study suggests that the use of a digital imaging system to capture images for either automated software analysis, or remote assessment by raters is suitable for epidemiological work. The use of recorded images enables study archiving, assessment by multiple examiners, remote assessment and objectivity due to the blinding of subject status.