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Color and pigment analyses are not required by the majority of olive oil marketing standards. However, it is a basic attribute that is highly associated, by most consumers, with the idea of quality. In this study, we aim at providing a fast non-invasive method for routine analysis that can be used on a large scale in the laboratories of olive oil analysis starting from pigment quantification and color range measurements. A selection of 172 virgin olive oil samples obtained in Tunisia between 2018 and 2019 were used for this purpose. Chlorophyll and carotenoid contents were analyzed using a UV spectrophotometer standard method while color range was measured using digital images taken under controlled conditions. All samples showed high significant differences in chlorophyll and carotenoid contents (p

Knowing the nitrogen status of crop is essential for agricultural operations management, nevertheless conventional methods are time consuming and expensive. In this research, possibility of using digital aerial images as a remote sensing method to determine the nitrogen content of sugarcane plant was studied. Arial images were captured from 3 sugarcane fields using a 12.9-megapixel digital camera mounted on a Phantom 3 quad-copter from 5 and 10 m heights. At the same time, four healthy top branches of sugarcane plants were cut from imaging points as plant samples. The nitrogen value of the samples was measured using Kjeldahl test at laboratory. Multilayer perceptron (MLP) artificial neural network (ANN) algorithm was used to estimate nitrogen status in the crop from the aerial digital images. Color indices of images were extracted using image processing in MATLAB software and their correlation with the nitrogen value were determined. The indices that had correlation with nitrogen were selected as inputs of the ANNs and the nitrogen value was the output. There was no significant difference between the nitrogen values predicted by ANNs and its actual values. The average errors of the ANNs training were 0.145 and 0.022 and the correlation coefficients of the predicted and actual values of nitrogen were 0.89 and 0.94, for 5 m and 10 m heights respectively. Also, the RMSE values of nitrogen estimation was 0.181 and 0.174, at 5 m and 10 m heights respectively. So, nitrogen estimation of sugarcane fields is possible by aerial digital imaging.

A new design of soil erosion and rainfall simulator is presented at this study as an attempt to deduce a mathematical formula of soil surface erosion phenomenon to describe the behavior of the sandy soil under the rainfall simulation, soil deformation such gullies and surface eroding rills are monitored by the Digital Close Range Photogrammetry (DCRP) technique that includes capturing digital images with a smart cellphone camera, and a Terrestrial Laser Scanner (TLS) to digitalize the soil surface as a point cloud data to produce Digital Elevation Models (DEM) with accuracy reaches 0.10mm with watershed, color relief, 3D surface model and contour maps. The results show that the mentioned methods give typical digital surface feature express especially of using geometrical adjustment that controls the orientation of the digital surface, it was clear that the digital imaging feature detection technology achieves accurate results despite its small cost compared to the laser scanner that the cost of imaging equipment and software are used did not exceed 12% if compared with the cost of the TLS method. As a result of the statistical observations, a simple mathematical formula was generated through digital photogrammetry method that describes the sandy soil behavior under the rainfall simulation as a relation between the eroding rate and the duration through the different gradient slopes.

Raw data acquired by imaging devices are converted into digital images by post-processing algorithms. However, these algorithms are significantly affected by numerous illumination conditions. Particularly, in the case in which illumination conditions depend on canonical light sources, unwanted light sources that locally illuminate the scene or mixed light from several light sources are recognized as the spatially varying illumination conditions. These complex illumination conditions cause several artifacts by affecting the digital image acquisition process. For example, optical aberrations are generated by refraction of complex light, or illumination estimation is significantly affected by the different color temperatures of multiple light sources. To overcome these problems, this study proposes an algorithm that decomposes the complex illumination from several light sources. First, the spatially varying illumination condition is discussed, and the artifacts generated by the conditions, such as false colors and aberrations, are analyzed. Second, mixed light from several canonical light sources is decomposed based on the imaging devices and spectral information of the canonical light sources. The proposed method has low complexity and increased applicability. Furthermore, the improvement scheme based on the proposed method has a parallelized structure and can be easily applied to various types of algorithms, such as color constancy, deconvolution, denoising, and contrast enhancement. The algorithms employing the improvement scheme show the potential of the proposed method for solving the problems associated with multiple light sources.

Because infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is therefore a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat-end condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we illustrate and demonstrate a method for assessing teat-end condition of dairy cows through digital images and software. A digital workflow has been designed where images of dairy cow teats are obtained and processed to display individual teats, and the cow and teat images are labeled and displayed through a graphical user interface. The interface then allows an evaluator to assess quarter- and cow-level teat-end condition and store the results for review and future analysis. The digital workflow permits several advantages such as the ability to perform remote teat-end condition assessments, and assess inter- and intrarater variability of teat-end condition scoring. We demonstrate the image-based teat-end condition assessment of 194 dairy cows that also had cow-side teat-end condition assessments by 2 expert evaluators. Weighted Cohen's kappa statistic (κ) was computed to measure the evaluators' concordance of categorical scores of quarter- and cow-level assessments when using cow-side and image-based assessments. Substantial agreement (0.61 ≤ κ ≤ 0.80) was observed between an evaluator's cow-side and image-based assessments at the quarter and cow level. Moderate agreement (0.41 ≤ κ ≤ 0.60) was observed between evaluators when using image-based assessments at the quarter and cow level. Near perfect agreement (κ = 0.89, 95% confidence interval 0.78-1.00) was observed between evaluators when using cow-side assessments at the quarter level, and substantial agreement (κ = 0.66, 95% confidence interval 0.53-0.79) was observed when using cow-side assessments at the cow level. This suggests that image-based teat-end condition classification is possible, and coupled with improvements in image acquisition and image processing, this method can be used to assess teat-end condition in a systematic and convenient manner.

Computer Vision (CV) is an interdisciplinary scientific field that discusses how computers can gain a high-level understanding of digital images or video. A system has been created that is capable of detecting a compact fluoresence lamp (CFL) light. However, in previous research there is no justification that the lamp is only a part that can glow on the lamp alone and has not been done in multi-lamp testing. This study aims to compare the lamp segmentation when it goes OFF and ON so that it could be justified the accuracy of this system and does multi-lamp testing. The method used is an experiment with collecting data by direct observation of the results of the system made. The system consists of a single board computer and a common webcam. The result is the difference between the lamp segmentation when it goes OFF and ON is small with the appropriate threshold setting. So that lamp light imaging had been made could function with good reability.

The digital image processing is one way to manipulate one or more digital images. Image segmentation has an essential role in the field of image analysis . The aim of this study was to develop an application to perform digital image processing of neutron digital radiographic images, hoping to improve the image quality of the digital images produced. The quality of edge detection could be used for the introduction of neutron digital radiographic image patterns through artificial intelligence. Interaction of neutrons with the matter mainly by nuclear reaction, elastic , and inelastic scattering. A neutron can quickly enter into a nucleus of an atom and cause a reaction. It is because a neutron has no charge. Neutrons can be used for digital imaging due to high-resolution information from deep layers of the material . The attenuated neutron beam in neutron radiography are passing through the investigated object. The object in a uniform neutron beam is irradiated to obtain an image neutron . The technique used in segmenting the neutron radiography in this study was a digital technique using a camera with a charge-coupled device (CCD), which was deemed more efficient technique compared to the conventional one. Through this technique, images could be displayed directly on the monitor without going through the film washing process. Edge detection methods were implemented in the algorithm program. It was the first step to complement the image information where edges characterize object boundaries. It is useful for the process of segmenting and identifying objects in neutron digital radiography images. The edge detection methods used in this study were Sobel, Prewitt, Canny, and Laplacian of Gaussian . According to the results of the image that have been tested for edge detection, the best image was carried out by the Canny operator because the method is more explicit. The obtained edges were more connected than the other methods which are still broken. The Canny technique provided edge gradient orientation which resulted in a proper localization .

A colorimetric method based on digital image processing and an image capture device as a detector has been developed for determining methylene blue (MB) in human and veterinary dosage forms. Operational variables, such as capture devices, angle and modes of capture as well as measuring vessel geometry, were evaluated in order to establish the most suitable conditions to obtain accurate measurements. Three different capture devices including a webcam, a compact digital camera and a smartphone camera were compared. Digital images were analyzed using the open source program ImageJ. Suitable results were achieved when using a 20.7 MP back camera of a smartphone and white-LED lighting conditions. Grayscale values were finally employed as response to determine MB concentration. Satisfactory linearity was achieved in the concentration range from 3.5 × 10–7 to 4.7 × 10–6 M, with the determination coefficient R2 > 0.97 and the limit of detection of 1.7 × 10–7 M. The proposed approach was successfully applied to the detection of MB in two commercially available dosage forms for human and veterinary treatment.

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.

As an important information medium, the digital image exists widely on the Internet. Quantum walks have the property of encrypting information. For the eneryption problem of optical digital images, an encryption scheme based on discrete cosine transform (DCT) and alternate quantum walks (AQW) is proposed in this paper. First, we use AQW and XOR operation to preprocess images in the spatial domain. Then, AQW are used to generate two random phase masks which can operate the preprocessed image and the DCT image, respectively. Finally, the encrypted image is obtained by using discrete cosine inverse exchange. The control parameters of AQW can replace the random phase mask as the key in the encryption and decryption process, so it is convenient for key management and transmission. The experimental simulation carried out the analysis of the image pixel histogram, the correlation of adjacent pixels, the robustness against noise and the sensitivity of secret keys, the results show that the image encryption method has strong security.

Pityrocarpa moniliformis is a tree species with socioeconomic potential in semiarid regions. The objective of this work was to perform the biometry of P. moniliformis seeds with digital processing of images as subsidies for studies of genetic divergence. For this, we executed the biometric analysis of the seeds from 33 adult trees using digital image processing. Subsequently, descriptive and correlation analyses were performed between the biometric variables, as well as the multivariate analysis of principal components and the Euclidean distance. Digital image processing is efficient in assessing biometric differences among adult trees which vary in the morphological and biometric aspects. The biometric variables quantified employing digital images are efficient in the distinction of the adult trees. For this reason, they are important morphological markers that can aid in the differentiation of genotypes of P. moniliformis and contribute to studies of genetic divergence.

Background: Radiologic diagnosis of external apical root resorption (EARR) is clinically important. Noise might disrupt this diagnosis. Therefore, we assessed the efficacy of noise reduction on periapical indirect digital radiography. Materials and Methods: This in vitro study as performed on 792 radiographs. A total of 66 single-rooted premolars were inserted in dried hemimandibles of sheep and fixed with modeling wax. Digital images were obtained using the parallel technique. The storage phosphor plates were processed in the DIGORA Optime scanner. The resulting images were sent to a computer using the Scanora software for radiographic analysis. The teeth were removed from the mandible, and artificial EARR defects were simulated. Afterward, the indirect digital radiographs were obtained at the same condition of the baseline. Five levels of noise reduction were applied. All images were saved in Digital Imaging and Communications in Medicine format and monitored by two observers twice over 2 weeks. Data were analyzed statistically using Cochran and McNemar tests (α = 0.05). Results: The highest sensitivity rate was found in the baseline group (0.99), and the lowest sensitivity was related to the “four-time noise reduction” method (0.91). The highest specificity rate was in the “five-times noise reduction” method (0.88) and the lowest specificity was associated with “one-time noise reduction” method (0.71). There was no statistical difference between images with/without noise reduction enhancement with varied gradation levels in terms of diagnostic accuracies of EARR (P > 0.05). Conclusion: Application of noise reduction procedure in Scanora software might have no effect on the accuracy of EARR diagnosis.

Background Digital imaging has the potential to improve diagnostic accuracy and make quantitative diagnoses. In the recent decades, software for radiographic analysis has been investigated and developed for the detection of lesions and the quantitative assessment of the depth of a caries lesion. In addition, the accuracy of diagnosis may also be enhanced by programs that filter the images. These programs can adjust the brightness and contrast, determine the gray level, invert the shades of gray, and apply pseudocolors. Few studies compared different types of digital images in the diagnosis of changes in the tooth crown. Aim The main aim of this study was to assess the the diagnostic accuracy of a direct digital radiography (DDR)-CMOS image with four types of filtered images for the detection of occlusal caries. Materials and methods Fifty randomly selected patients' teeth were clinically examined and digitally radiographed. Radiographed images are converted into four filter images with the help of software. Filtered images were then selected for inter- and intraobserver examination and the result was subjected to statistical analysis. Conclusion DDR-CMOS and negative image were found to be more useful in diagnosing occlusal caries.

This study develops a model based on Rayleigh Damping (RD) with potential use in breast cancer diagnostics. Displacement data of over 14,000 reference points on the breast surface from 14 breasts was captured using the Digital Image Elasto Tomography (DIET) system. The reference points were split into four segments and an ellipse fit utilized to calculate the work done and consequent viscous damping constant for each reference point. Fitting a model based on RD to median filtered data gave consistent results for one model coefficient across all breasts. The other coefficient was seen to have diagnostic potential when the model was fit to unfiltered data, and is the focus of this paper. The coefficient value was compared between breast segments adjacent to and containing the tumor (locations given from X-ray mammography) to those opposite the tumor. A total of 11 out of 14 breasts had a higher coefficient found in the tumor segment and all breasts had a higher coefficient in at least one adjacent segment. This method showed potential for breast specific diagnosis and tumor localisation using the DIET system.

Cytological study is a highly specialized type of laboratory analysis of the cellular composition of biological material and is to assess the morphological characteristics of cellular elements. The modern development of digital technologies is increasingly forming the interest of specialists to such a section as telepathology (digital pathology), which is a process of virtual microscopy with the transformation of classical cytological preparations into digital. Most morphologists currently use some forms of digital imaging, such as static images obtained by optical cameras mounted under a microscope. The development of more high quality image and resolution in the digital pathology promotes the use of telepathology, including telecitology in their daily work for training specialists, counselling of medications, monitoring the quality of diagnosis.

The existence of internal structural defects in gas insulated combinations (GIS) seriously affects the normal operation of equipment, and it needs an efficient and non-destructive detection method. This study is focused on the feasibility of X-ray digital imaging detection in GIS equipment monitoring. First, the digital imaging detection process is briefly introduced. Then, in order to obtain high-quality digital images, a correlation coefficient is introduced to improve the nonlocal mean filtering denoising algorithm. In terms of image enhancement, a histogram equalization method is introduced, and then an improved contrast-limited adaptive histogram equalization (CLAHE) method is proposed for image denoising. The detection system and image processing method of this study are used to detect two GIS equipment units of the Chongqing Electric Power Company (China). It is found that this method clearly and accurately detects the equipment defects with high reliability. This study provides some theoretical basis for further promotion of the X-ray digital radiography in GIS equipment defect detection.

Archive color photographs—particularly of sites where sediment is no longer available—are an underused resource that potentially contains detailed paleoenvironmental information. To investigate this potential, two sets of digital images were taken, at different times, of loess, glaciolacustrine, and deep-sea sediments. The first image set was taken using standard digital cameras. Lighting conditions and sediment surface preparation varied, in a similar way to characteristics likely to be encountered in archive photographs. The second image set was taken by a high-resolution, line-scan camera with an integrated light source. CIELAB (Commission Internationale de l'Eclairage) color reflectance data were obtained from both image sets and analyzed by cross correlation. Of the three reflectance parameters (L*, a*, and b*), L* reflectance is the most compromised by differences in ambient lighting or moisture content. Textural distinctions appear to be an important factor influencing the cross correlations and produce multiple, relatively weak solutions for the glaciolacustrine sediments, whereas the texturally uniform loess and deep-sea sediments produce a single, best-fit solution. Comparison of a* reflectance records from an undated marine sequence with a chronologically constrained sequence from the same site indicates the potential to apply color reflectance to produce preliminary age models.

Background: Surgical site infection (SSI) continues to be a common and costly complication after surgery. The current commonly used definitions of SSI were devised more than two decades ago and do not take in to account more modern technology that could be used to make diagnosis more consistent and precise. Patient-generated health data (PGHD), including digital imaging, may be able to fulfill this objective. Methods: The published literature was examined to determine the current state of development in terms of using digital imaging as an aide to diagnose SSI. This information was used to devise possible methodology that could be used to integrate digital images to more objectively define SSI, as well as using these data for both surveillance activities and clinical management. Results: Digital imaging is a highly promising means to help define and diagnose SSI, particularly in remote settings. Multiple groups continue to actively study these emerging technologies, however, present methods remain based generally on subjective rather than objective observations. Although current images may be useful on a case-by-case basis, similar to physical examination information, integrating imaging in the definition of SSI to allow more automated diagnosis in the future will require complex image analysis combined with other available quantified data. Conclusions: Digital imaging technology, once adequately evolved, should become a cornerstone of the criteria for both the clinical and surveillance definitions of SSI.

With the massive increase of online content, widespread of social media, the popularity of smartphones, and rise of security breaches, image forensics has attracted a lot of attention in the past two decades alongside the advancements in digital imaging and processing software. The goal is to be able to verify authenticity, ownership, and copyright of an image and detect changes to the original image. However, more sophisticated image manipulation software tools can use subtle anti-forensics techniques (AFTs) to complicate and hinder detection. This leads security professionals and digital investigators to develop more robust forensics tools and counter solutions to defeat adversarial anti-forensics and win the race. This survey study presents a comprehensive systematic overview of various anti-forensics and anti-AFTs that are proposed in the literature for digital image forensics. These techniques are thoroughly analysed based on various important characteristics and grouped into broad categories. This study also presents a bibliographic analysis of the-state-of-the-art publications in various venues. It assists junior researchers in multimedia security and related fields to understand the significance of existing techniques, research trends, and future directions.

Effective measurement of dense granular flow rates is essential for ensuring optimal performance of a wide variety of industrial processes with digital imaging processing techniques being developed and implemented in many manufacturing control applications. This paper presents a digital image flow meter system that utilizes sequential image pairs to determine granular mass flow rates with a comparison of two different computational strategies: Direct Regression (DR) of the displacements and Neural Network (NN) modeling. Results show DR is a robust method that can accurately predict flow rates with an average relative error of 7.56% without calibration despite its simplicity. Both methods can have the relative error reduced below 3% by time-averaging over a series of measurements. NN models were found to predict flow rates from image pairs faster than DR, but the NN predictions had a higher variance and lower accuracy. The proposed granular flow metering strategy has the potential of utilizing inexpensive hardware to effectively estimate flow rates and can be easily implemented in hardware platforms where there is a visible granular flow.

A digital image (DI) method is reported to determine the transmittance and the uniformity of transparent optical materials (TOMs) at the same time, in which an objective image (OI) with a two dimensional (2D) entropy of 3.45 is scanned using a scanner with a black background. The OI pictures covered without and with a TOM went through gamma correction and color correction. The two corrected pictures were transformed into two matrixes, between which the transparency ratio and the correlation coefficient refer to the transmittance and the uniformity of TOMs. As a result, a p-value of 0.97 and an r value of 0.92 were achieved from the paired T-test between the DI method and the ultraviolet spectrometry (UVS) method, indicating a similar accuracy in determining the transmittance of TOMs between them. In addition, the DI method is a simple and rapid method to evaluate the uniformity of TOMs and to reveal the correlation among transmittance, uniformity and thickness of TOMs, particularly applicable for inhomogeneous TOMs.

Based on the application scenarios of space target imaging simulation, this paper conducts modeling research on the digital imaging simulation method of space target navigation camera. According to the geometric positioning model and the optical characteristics of different materials, a model of the target geometry and optical characteristics was carried out. Finally, a digital imaging simulation software was designed and developed based on the OptiX engine. Through the analysis of the image quality test and the speed test, it is verified that the digital imaging simulation method of the space target navigation camera can complete the imaging simulation task correctly and quickly.

Automatic image annotation has increasingly exerted a tremendous fascination on researchers with the development of digital imaging in recent years. First, most works exploit the sufficient labeled data to train the models and trigger the unfavorable experimental performance in semi-supervised learning. Second, some examinations solve the semi-supervised problem only by a samples graph or tags graph, limiting in improving the annotation results owing to the incomplete data structure. To this end, we propose a method called "Semi-supervised Image Annotation with Parallel Graph Convolutional Networks (SPGCN)". This algorithm combines graph convolutional networks (GCN) with image annotation to promote annotation performance under semi-supervised learning. Furthermore, SPGCN, connecting the tags graph with the samples graph, is proposed to improve annotation results, further considering tags’ distribution and features’ distribution to aggregate the features. Experiments on three benchmark image annotation datasets show that our approach outperforms other existing state-of-the-art methods.

Retinal imaging plays a very significant role in the study of retinal vasculature changes indicative of chronic disease information related to vision. This study investigates the invulnerability of retinal image information in the disease information system and assesses the quantitative method of the morphological changes in the retinal vascular network. In this work, the medical digital image transmission protocol Digital Imaging & Communications in Medicine (DICOM) version 3.0 and the retinal image Picture Archiving & Communication System (PACS) were constructed in the laboratory using browser/server mode. Also, the DICOM-SR document was designed in this article using a list or hierarchy, and the retinal images to report the information of patients by using the Hypertext Transfer Protocol (HTTP) – based Web Access to DICOM Persistent Objects (WADO) approach. The results showed that the retinal image PACS system constructed in Browser/Server mode can effectively store and transmit DICOM images. When the imaging device is combined with the application program, special adapters are used to negotiate the transmission syntax. The message flow is decoded in the communication process, which can be connected with the realization to improve the efficiency of information collection. The proposed PACS system integrates the quantitative features of retina providing more meaningful research data for data mining in comparison to the traditional state of the art methods based on chronic disease management system. The diagnostic ability of the retinal imaging procedure using the DICOM images is justified by obtaining 98.51%, 98.04%, 99% and 99.01% of accuracy, sensitivity, specificity and precision values respectively.

Due to the increased revolution of digital technology, the process of information sharing, accessing becomes easier. But securing this information is the major critical task. The major threat is occurred in digital images by making forgeries. Several existing techniques are utilized for detection the forgeries in digital images. But still, it lacks inaccurate detection. Hence a novel technique is designed for detecting the forged images accurately. The main motive of this research is focused on detect image forgery and localize the forged region accurately. Initially, the input images obtained from digital image acquisition and the selected images are isolated as an overlapping patch. Polar Cosine Transform (PCT) with orthogonal kernel and Local Binary Pattern (LBP) approaches are used to extract features from these patches. From the features extracted from the PCT approach, the patches are detected using Multidimensional Spectral Hashing techniques (MSH) and the forged patches are filtered out. Alternatively, geometry-based image forgery detection is carried out using the LBP extracted features. Finally, the forged regions are located and detected in the digital image. The proposed approach's efficiency is measured and compared to current techniques

A novel imaging technology in which a see-through screen acts as a camera is proposed based on the optical system using a waveguide holographic optical element and digital image reconstruction.

As a common way of information acquisition, digital image acquisition has a wide range of applications. In daily life, due to the limitation of the scene, there will be diffuse reflection in the process of image acquisition, which leads to the presence of artifacts or highlights in the process of image acquisition and affects the quality of image acquisition. In order to suppress the above situation, the suppression effect of homomorphic filtering on artifacts is studied, and the filtering effects of ideal low-pass, Gaussian low-pass and Butterworth low-pass are compared. In Butterworth low-pass filtering, the cut-off frequency has an obvious effect on the suppression, and an adaptive cut-off frequency is designed. Through experimental comparison, the designed adaptive cutoff frequency has a good effect on the suppression of artifacts.

With the development of LLL night vision technology from the second and third generation to 4G, the specifications of imaging have been significantly improved. In particular, the contrast of imaging, which determines the clarity, even resolution of the image. The contrast of imaging also refers to the Stray Light Crosstalk (SLC) among optical fibers. How to characterize the contrast of Optical Fiber Imaging Elements (OFIE) by detecting the SLC has become an important problem that must be solved. At present, the contrast performance is often characterized by the Knife-edge Response Value (KERV), which is the transmittance value of light passing through the knife edge through optical fiber imaging element. However, KRV has some disadvantages, such as inaccurate measurement value, harsh test conditions, complex sample preparation and great influence on the measurement result. The most important disadvantage is that KRV is an indirect detection, which needs to slice and grind the tested sample, and the slice position often cannot represent the overall contrast performance of the tested OFIE. In this paper, the digital imaging equipment (high-precision CMOS camera + high-resolution microscope) is used to take photo of the end face of the OPIE placed on the black-and-white boundary of the USAF resolution target. The process of light passing through the black-and-white edge provides accurate information for the contrast change. Through the computer analysis and processing of the digital image, the SLC in different positions of the OPIEs is obtained. The SLC can be used to analyze the degree of crosstalk, or contrast. The digital imaging equipment mainly includes light source system, precision transmission system, CCD camera system, software analysis system and control system. The equipment has the advantages of direct detection, simple operation, high precision, good repeatability and reliability, convenient maintenance, and can be used to test and analyze the imaging contrast of all optical fiber imaging elements. It has been proved that the device is effective in detecting the SLC, and completely replaces the KRV method.

Due to the continuous improvement of digital imaging technology, making counterfeit banknotes has become easier and cheaper. In order to solve the problem of counterfeit banknotes, the main purpose of this article is to develop a banknote detection system (BDS). This system will detect whether banknotes are included in the digital image during scanning or printing, reducing the use of high-resolution digital imaging products for counterfeit banknote manufacturing opportunity. The system is divided into three main steps: magnetic detection, fluorescent detection and watermark detection. In the future, this system can be implanted in image-related computer peripherals (such as scanners, printers or multi-function printers) to scan banknotes at the beginning or prevent blockages when copying banknotes.

In computer vision, there are many methods of reconstructing the image point. 3D reconstruction from digital image sequence of scene or object is a difficult and important task in computer vision. However, such a reconstruction requires a large computational effort for finding the point of correspondence between different views. Furthermore, the accuracy should not be reduced in case of noisy data. There is one important technique to digitization of physical object which is binocular stereo vision. From two subsequent digital images of the physical object taken from different viewpoints, we can make a 3D virtual model for the physical object by using this approach. Basically, the common processes for binocular stereo vision comprise digital image acquisition, camera’s calibration, feature point extractions, feature points matching and 3D reconstructions. In this paper, we have discussed the problem of varying camera parameter in the process of matching and reconstruction. we have the studied the problem and how it affects when the camera parameter varies in the process of matching; two types of parameter are as follows: intrinsic parameter and extrinsic parameter; image setup and some equation help to set the parameter, and a robust algorithm is proposed for reconstructing free-form space.

Statistical image analysis of an ensemble of digital images of histological samples is performed as an auxiliary investigation a result of the recently proposed method of articular cartilage repair utilizing growth plate chondrocytes in a skeleton animal model. A fixed–shift model of maximal likelihood estimates of image histograms applied for monochromatic (grayscale) images or their RGB components confirms the statistically significant effect of the previously proposed medical treatment. The type of staining used to prepare images of histological samples is related to the visibility of the effectiveness of medical treatment. Hellinger distance of escort distributions for maximal likelihood estimates of image histograms of medically treated and control samples is investigated to identify grayscale (or RGB) intensities responsible for statistically significant difference of the estimates. A difference of Shannon entropy quantifying informational content of the histograms allows one to identify staining and image colors which are most suitable to visualize cluster formation typical for articular cartilage repair processes.

Content-based Image Retrieval (CBIR) system uses the visual information and features present within an image, to find the most analogous images from any gigantic digital image data-set effectively and efficiently, as per the users requirements. Nowadays, the immense advancements in the field of Digital Imaging have exponentially increased the real-time applications of the CBIR techniques. Researchers around the globe are using different CBIR techniques in the field of education, defense, agriculture, remote sensing, satellite imaging, biomedical research, clinical care, and medical imaging. The Major objectives of this chapter are to provide a brief introduction to the different CBIR techniques and their applications on medical image retrieval. This chapter mainly focuses on the current Machine Learning (ML) and Deep Learning (DL) techniques to address the different issues and limitations of the traditional retrieval systems. Initially, we have discussed the different hand-crafted image features based retrieval systems to understand the perspectives of this research field. Here, we aim to congregate the weaknesses and constraints of the conventional retrieval systems and respective solutions with the help of the advanced DL algorithms. Researchers have suggested several CBIR techniques to improve the efficiency of the retrieval of medical images. In this chapter, a review of some state-of-the-art retrieval techniques and respective future research directions are provided.

A multi-cascade adaptive optical system for imaging and image stabilization for the Large Solar Vacuum Telescope is described. This system was created in 2017 by specialists of the V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, with the technical support of the Institute of Solar-Terrestrial Physics SB RAS, Irkutsk. The system has been tested at the Large Solar Vacuum Telescope (Baikal Astrophysical Observatory) and demonstrated its efficiency. Along with the first cascade of adaptive image stabilization by a tip-tilt corrected mirror, this system employs the second imaging cascade based on correction with a flexible mirror controlled by a specialized wavefront sensor, as well as the third cascade for real-time post-detector processing of video camera frames. Reliable experimental data confirming the efficiency of the multi-cascade adaptive system for image formation and stabilization have been obtained. Three highrate digital video cameras recording simultaneously digital images with rates from 300 to 980 frames per second were used to test the system. The mirror correcting wavefront tilts and operating in a closed optical feedback loop was controlled by the specially developed software including the fast correlation tracking algorithm. The post-detector digital imaging was performed with a special software for processing of video camera frames in real time with the use of modern high-speed parallel algorithms based on the Intel MKL and IPP libraries.

Insulation faults in high-voltage applications often generate partial discharges (PDs) accompanied by corona activity, optical radiation mainly in the ultraviolet (UV) and visible bands. Recent developments in low-cost, small-size, and high-resolution visible imaging sensors, which are also partially sensitive to the UV spectral region, are gaining attention due to their many industrial applications. This paper proposes a method for early PD detection by using digital imaging sensors, which allows the severity of insulation faults to be assessed. The electrical power dissipated by the PDs is correlated to the energy of the acquired visible images, and thus, the severity of insulation faults is determined from the energy of the corona effect. A criterion to quantify the severity of insulation faults based on the energy of the corona images is proposed. To this end, the point-to-plane gap configuration is analyzed in a low-pressure chamber, where digital image photographs of the PDs are taken and evaluated under different pressure conditions ranging from 10 to 100 kPa, which cover the typical pressure range of aeronautic applications. The use of digital imaging sensors also allows an early detection, location and quantification of the PD activity, and thus assessing the severity of insulation faults to perform predictive maintenance tasks, while enabling the cost and complexity of the instrumentation to be reduced. Although the approach proposed in this paper has been applied to detect PDs in aeronautic applications, it can be applied to many other high-voltage applications susceptible of PD occurrence.

The case of image manipulation is rising, along with the growth of digital imaging development. One of the many dangerous image manipulation types is a copy-move forgery. In this manipulation, the user can cover some parts in images with another piece in the same picture. SIFT keypoint extraction is one of many methods used by many researchers to detect whether a digital photo is copy-move manipulation result or not. Usually, this keypoint extraction method had combined with other methods for improving its accuracy. This paper explained our experiment with Gaussian Pyramid Decomposition, Compact Watershed segmentation mask, and DBSCAN clustering in copy-move forgery with SIFT. This combination shows the average precisions at more than 78% and can detect in shorter average times.

Digital imaging using conventional camera, mobile phone camera, or computer scanner can be a convenient and low-cost approach for the highly sensitive assessment of hexavalent chromium in natural and waste waters. An aliquot sample containing Cr(VI) is reacted with diphenylcarbazide to yield a characteristic violet color. Image analysis of the violet-colored product gave the red, green, and blue (RGB) intensities that are converted into ten uniform and non-uniform color spaces and two color difference parameters, which are used and compared here, for the first time, as analytical signaling tools for the assessment of hexavalent chromium. Many of the acquired signals displayed outstanding analytical performance and statistically surpassed the regularly used RGB signals and competed well with signals of a sophisticated Shimadzu 1650 PC spectrophotometer. The sensitivity and simplicity and low cost of this strategy make it an outstanding competitor for commercial hexavalent chromium analyzers and advanced spectrophotometers.

Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

In order to characterize the performance of visible digital imaging systems in the laboratory, in the field and in simulation, the use of fractal test-targets has been optimized. This work is based on the previous achievements in the use of binary fractal targets (2014) and the Corner-Point (CP) resolution criterion (2017), for DRI range modeling of optronic cameras. The principle is to resume from the process of multi-scale fractal calculation of the binary target, to extend it in the case of a multi-level of gray. The distribution of CP contrasts by scale is then adapted to two constraints, on the one hand the measurement accuracy and on the other hand the criterion definition of the operational task evaluated for the camera. A target will be specifically designed to accommodate an operational need, such as the identification of vehicles or handheld weapon. The exploitation of the fractal target degraded by the imager is carried out by the comparison of CP by scales with the original target, after an image registration phase, facilitated by an original Yin-Yang design of the target at its lowest CP scale. The main metric for assessing DRI range is the Resolved Contrast Function (RCF), obtained from the multi-scale CP Probability of Correct Resolution. In the first part of the paper, the principles of design and exploitation of the target are presented, applied to an example of a DRI range assessment of a camera coupled to image restoration processing. In a second part, the use of this evaluation technique is developed in the example of digital image fusion systems, from two bands with its own optronic characteristics and some non-linear digital processing. This work, carried out in simulation using the FUSIM software, allows to establish a selection of the optimal combinations (pre-processing, fusion processing) offering the best RCF.

Infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows; therefore, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we demonstrate a digital teat-end condition assessment by way of deep learning. A total of 398 digital images from dairy cows' udders were collected on 2 commercial farms using a digital camera. The degree of teat-end hyperkeratosis was scored using a 4-point scale. A deep learning network from a transfer learning approach (GoogLeNet; Google Inc., Mountain View, CA) was developed to predict the teat-end condition from the digital images. Teat-end images were split into training (70%) and validation (15%) data sets to develop the network, and then evaluated on the remaining test (15%) data set. The areas under the receiver operator characteristic curves on the test data set for classification scores of normal, smooth, rough, and very rough were 0.778 (0.716-0.833), 0.542 (0.459-0.608), 0.863 (0.788-0.906), and 0.920 (0.803-0.986), respectively. We found that image-based teat-end scoring by way of deep learning is possible and, coupled with improvements in image acquisition and processing, this method can be used to assess teat-end condition in a systematic and efficient manner.

Objectives: Despite the widespread of assessment of smile aesthetic perception in many areas, there has yet to be a direct comparison of digital and paper-based photographs for the assessment of smile aesthetics. Here we compared digital and paper-based photographs representing different smile aesthetic features using visual analog scale (VAS) scoring. Materials and Methods: One hundred students were randomly recruited from a university campus. Participants were asked to record their perception of smile aesthetics via paper and digital-based platforms. The minimum clinically important difference between platforms was set at 15 mm. The percentage of participants who rated smile attractiveness worse on digital images was recorded. The paired one-tailed Student’s t test was used to determine differences between digital and paper platforms, and Bland–Altman analysis and intraclass correlations (ICCs) were used to test for agreement between paper and digital photographs. Results: Ninety-nine subjects participated, 55 men (mean age = 22.05, standard deviation [SD] = 1.91) and 44 women (mean age 22.05, SD = 1.84). There were statistically significant differences between paper-based and digital photographs for all images except one (paired t test; P < 0.05). Digital ratings were lower than paper-based ratings for all images, and differences were clinically significant in four out of eight images. A high percentage of participants (50.5%–85.9%) rated smile attractiveness worse on digital images than on paper for all images. There was poor agreement between the two methods as assessed by ICCs and Bland–Altman analysis. Conclusion: Equivalence between paper and digital images for smile aesthetics cannot be assumed, and paper-based photographs may lead to clinically relevant overestimations of perceived attractiveness. As academic dentistry increasingly relies on digital imaging and sharing in the post-COVID-19 world, further validation of digital platforms for smile aesthetics assessment is warranted, and care should be taken when interpreting the results of studies assessing smile perception based on different platforms.

Background and Aims The current methods for measuring the anthocyanin concentration (AC) and antioxidant activity (AA) of grape juice have limitations. The objective of this work was therefore to evaluate the application of digital imaging as a simple, fast and low‐cost method for determining AC and AA in juice. Methods and Results The AC and AA of 75 grape juice samples were measured using reference methods and then correlated against digital images of 200 dpi resolution obtained with a scanner, using partial least squares regression. The samples were separated with the Kennard–Stone algorithm into calibration sets (50 samples) and external validation sets (25 samples). The models used for the quantification of AC and AA presented a root mean square error of calibration of 8.8 mg/L and 9.8 μmol and a root mean square error of prediction of 7.5 mg/L and 8.0 μmol, respectively. The correlation coefficients for the adjustment were 0.8479 and 0.9258 for the models in the determination of AC and AA, respectively. Conclusions Digital imaging and multivariate calibration can be used to measure efficiently selected parameters of grape juice composition. Significance of the Study Analytical methods based on digital imaging offer promising alternatives to conventional methods, since they are fast, simple, non‐destructive and employ little or no chemical reagents, thereby reducing analysis costs and facilitating in situ analysis.

The present work proposed the application of a multivariate regression model based on image data to monitor the decolorization process. Thus, a PLS regression based on the color histogram was applied to monitor the methylene blue degradation by the Fenton reaction. The results obtained by the digital imaging and UV-Vis methods were compared and the initial (Cº) and final (C) methylene blue concentrations, as well as the kinetic parameters, coefficients of determination (R2), half time degradation (t½), intercept (ρ), and slope (σ), were evaluated. From our results, the digital imaging and UV-Vis methods have equivalent potential to monitor the color removal profile, similar kinetic term, and low measurement errors. While the coefficient of determination (R(2)) of all PLS models and kinetics curves are close to 1.00, the half time degradation (t½) parameter ranged between 0.29 to 1.39 min for the UV-Vis model, and 0.80 min to 2.17 min for the digital imaging model. Furthermore, the efficiency of methylene blue removal ranged between 92.04% and 97.78% for the UV-Vis model and 91.30% to 93.72% for the digital imaging model. Then, based on statistical comparison tests, it was concluded that the digital imaging method is an alternative to monitor dye degradation processes.

Introduction There is an emerging need for telecytology in Colombia as the demand for cytopathology has increased. However, due to economic and technological constraints telecytology services are limited. Our aim was to evaluate the diagnostic feasibility of using whole slide imaging with and without Z-stacking for telecytology in Colombia, South America. Methods Archival glass slides from 17 fine needle aspiration smears were digitized employing whole slide imaging (WSI) (Nanozoomer 2.0 HT, Hamamatsu) in one Z-plane at 40x, and panoramic digital imaging (Panoptiq system, ViewsIQ) combining low-magnification digital maps with embedded 40x Z-stacks of representative regions of interest. Fourteen Colombian pathologists reviewed both sets of digital images. Diagnostic concordance, time to diagnosis, image quality (scale 1-10), usefulness of Z-stacking, and technical difficulties were recorded. Results Image quality scored by pathologists was on average 8.3 for WSI and 8.7 for panoramic images with Z-stacks (P = .03). However, diagnostic concordance was not impacted by image quality ranking. In the majority of cases (72.4%) pathologists deemed Z-stacking to be diagnostically helpful. Technical issues related to Z-stack video performance constituted only a minor proportion of technical problems reported. Slow downloads and crashing of files while viewing were mostly experienced with larger WSI files. Conclusion This study demonstrated that international telecytology for diagnostic purposes is feasible. Panoramic images had to be acquired manually, but were of suitable diagnostic quality and generated smaller image files associated with fewer technical errors. Z-stacking proved to be useful in the majority of cases and is thus recommended for telecytology.

The rise of the digital imaging is remarkable, and the methods and techniques of image processing and analysis of the digital one must also accompany this technological evolution. In a line of research on the moments theory associated with digital imaging, values are extracted from digital images for the needs of classifications or even of reconstruction, as unique descriptors of an image, our work fits. In this paper, we propose a new method, fast and efficient, for calculating orthogonal moments on the discrete 3D image. We opted for the orthogonal polynomials of Meixner and for a new representation of the 3D image by cuboids having same gray levels called image cuboid representation. Based on this representation, we calculate the moments on each cuboid before summing all cuboids in order to obtain the global moments of a 3D image. Through a set of simulations, we prove that our method allows to reduce the time required for the calculation of moment on a 3D image of any size and any order, but not only, this method makes it possible to improve the quality of 3D image reconstruction from low-order moment.

A simple, accurate, and low-cost analytical procedure for captopril determination through digital imaging is presented. The method relies on the spot test reaction between captopril and palladium (II) chloride, which produces a yellow and water-soluble complex with maximum absorption at 380 nm. A smartphone camera and a portable apparatus built for internal lighting control were put together to acquire digital images of reaction mixtures. Digital image processing through the RGB approach was used to establish a quantitative relationship between color intensity and captopril concentration. Under the most suitable operational and experimental conditions, an analytical curve was built monitoring the Blue channel within the concentration range of 3.12 × 10−5 to 1.21 × 10−3 mol L−1. Limits of detection and quantification were equal to 8.06 × 10−6 and 2.69 × 10−5 mol L−1, respectively. Recovery percentage in synthetic urine samples ranged from 97.1% to 102.9%. Results were compared with a reference method and no significant differences were detected at the 95% confidence level. The developed method presents budgetary and environmental advantages concerning the use of cheap and easy-handled devices and the consumption of very low volumes of reagent (800 μL per determination). It can be a useful analytical tool for laboratories with limited financial resources while abiding by green chemistry principles.

An iPod-based digital image colorimeter was developed for the determination of lead(II) in cassava. The method is based on the color values of a lead(II) solution following its reaction with dithiz...

With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous. This ease of digital image capture and sharing is also accompanied by widespread usage of user-friendly image editing software. Thus, we are in an era where digital images can be very easily used for the massive spread of false information and their integrity need to be seriously questioned. Application of multiple lossy compressions on images is an essential part of any image editing pipeline involving lossy compressed images. This paper aims to address the problem of classifying images based on the number of JPEG compressions they have undergone, by utilizing deep convolutional neural networks in DCT domain. The proposed system incorporates a well designed pre-processing step before feeding the image data to CNN to capture essential characteristics of compression artifacts and make the system image content independent. Detailed experiments are performed to optimize different aspects of the system, such as depth of CNN, number of DCT frequencies, and execution time. Results on the standard UCID dataset demonstrate that the proposed system outperforms existing systems for multiple JPEG compression detection and is capable of classifying more number of re-compression cycles then existing systems., 12 pages

The sensor pattern noise has been broadly used to uniquely recognize digital imaging gadgets. However, the presence of some peaks in Fourier domain and low-frequency defects that are shared among cameras due to the same or comparable in-camera processing strategies leads to increasing the false acceptance rate. In this way, it is important to eliminate these undesirable artifacts to enhance the exactness and reliability. In this letter, we have developed a method for preprocessing the camera reference pattern noise (RPN). This letter is motivated by the fact that refraction of light on dust particle and optical surface affect the major part of camera-RPN. These components are combined and termed as “doughnut” patterns. They are of low frequency in nature. As these low-frequency defects are not characteristic of the sensor, hence they should be removed from the estimated camera-RPN. To achieve this, the proposed method first uses the widely accepted spectrum-equalization algorithm (SEA) to find and suppress the peaks present in the camera-RPN and then eliminates the low-frequency defects in the discrete cosine transform domain. Experimental results performed on the freely available “Dresden” image database show that the proposed method is able to improve the efficiency of the SEA methods, as well as this combination is able to work far better than the other camera-RPN enhancement techniques.

The popular histogram equalisation (HE) technique, which was developed to improve the image contrast, sometimes may also be misused to hide intensity variations in tampered images with ill intention. The authors investigate how existing image forensic techniques may fail to detect HE operation in highly compressed and low-resolution images. They then propose an algorithm to detect whether a given image (either uncompressed or JPEG compressed) has undergone the HE process or not. It is based on the frequency domain analysis of image histogram and exploits the difference in DC and AC coefficients in histogram's discrete Fourier transform. It can detect HE operation even if the image is saved in JPEG format after the equalisation, where most the existing algorithms fail. The extensive computer simulations over large dataset show the effectiveness of the proposed algorithm.

In recent years, end users can easily capture digital images using several devices, such as smartphones, mobile devices and digital imaging cameras, allowing such images to be easily copied, manipulated, transmitted or format converted without any restrictions. This fact suggests the necessity to develop digital tools, such as digital watermarking, to solve the issues associated with copyright protection and ownership authentication of digital images. To claim the ownership of a digital image, we propose a camouflaged, unseen-visible watermarking technique based on luminance and texture properties in conjunction with an image enhancement criterion. The proposed method has some advantages over invisible and visible watermarking methodologies in terms of readability and imperceptibility of the watermark, respectively. The experimental results demonstrate that the proposed scheme is effective and applicable for digital images on a variety of topics, including natural scenes and man-made objects, both indoors and outdoors. A comparison with previously reported methods based on unseen-visible watermarking techniques is also provided.