High-definition Images Research Articles

Developing a Demonstration Video on Making Dry Banana Preferred by Generation Z

The student of vocational high school nowadays are classified as generation Z. They are common to use information and communication technology. They prefer audio-visual learning media rather than podcast recording or text media. This study aimed to develop a video on making dry banana for the Agriculture Product Processing course and assessing the video’s feasibility. This study was a research & development (R&D) study using the 4D (define, design, develop, and disseminate) approach. Analysis of content, curriculum, and student needs was carried out in the define stage. The scriptwriting and production team selection was conducted in the design stage. Video recording, editing, and feasibility test from experts were performed in the development stage. The video was evaluated by two content experts and one media expert. In the disseminate stage, thirty students as prospective consumers evaluated the video feasibility. The data were analyzed descriptively. The video was in the mp4 format and had a duration of 13 minuter and 13 seconds. The video has been provided with high-definition images, captions, pictures, 2D animations, transitions, and effects. The experts and the students rated this video to be very feasible to be used as instructional media.

Assessment of Urothelial Cytotoxicity at Morphological and Molecular Levels: Urinary Bladder as Target Organ.

The urinary bladder is a target organ of several toxic agents. Exposure to those agents induces mild-to-severe changes, which can be evaluated by different methods. Among them, the scanning-electron microscopy (SEM) is the "gold standard" for characterizing urothelial damage since it provides high-definition images, making it possible to detect early lesions on the surface of the urinary bladder. In addition, molecular technologies allow detecting changes in genetic material and investigating the interaction between genes and environmental stress in disease causation. The urinary bladder epithelium is where the most common type of bladder cancer occurs in humans, that is, the transitional-cell carcinoma (TCC). In animal models, the TCC can be similar to the disease in humans. Techniques to evaluate urothelium in experimental models aid in the comprehension of risk factors for urothelial carcinogenesis.

Diagnostic Ability of High-definition Imaging Using Ultraslim Endoscopes in Early Gastric Cancer.

PurposeIt is unclear whether high-definition (HD) imaging improves visibility and diagnostic ability in early gastric cancer (EGC) compared with standard-definition (SD) imaging. We aimed to compare the diagnostic performance and visibility scores of HD and SD ultraslim endoscopes in EGC.Materials and MethodsWe used HD and SD ultraslim endoscopes to obtain 60 images with similar compositions of gastric environments. Of the 60 images, 30 showed EGC (15 images for each modality) and 30 showed no EGC (15 images for each modality). Seventeen endoscopists evaluated the presence and location of the lesions in each image. Diagnostic ability was compared between modalities. The color difference between a lesion and the surrounding mucosa (ΔE) was measured and compared between the modalities.ResultsThe ability of HD to detect EGC was significantly higher than that of SD (accuracy: 80.8% vs. 71.6%, P=0.017; sensitivity: 94.9% vs. 76.5%, P<0.001; positive predictive value, 76.2% vs. 55.3%, P<0.001; and negative predictive value (NPV), 94.1% vs. 73.5%, P<0.001). The ability of HD to determine the horizontal extent of EGC was significantly higher than that of SD (accuracy: 71.0% vs. 57.8%, P=0.004; sensitivity: 75.3% vs. 49.0%, P<0.001; NPV, 72.9% vs. 55.9%, P<0.001; and area under the curve: 0.891 vs. 0.631, P=0.038). The mean ΔE was significantly higher for HD than for SD (10.3 vs. 5.9, P=0.011).ConclusionsThe HD ultraslim endoscope showed a higher diagnostic performance in EGC than the SD endoscope because it provided good color contrast.

Novel Stream Ciphering Algorithm for Big Data Images Using Zeckendorf Representation

Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real‐time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high‐definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key “look random.” This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high‐definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb’s randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.

Real-Time High Definition License Plate Localization and Recognition Accelerator for IoT Endpoint System on Chip

ABSTRACT Automatic License Plate Recognition (ALPR) systems have become popular application areas of the Internet of Things (IoT). A typical ALPR system always needs powerful processors such as Cortex-A7. However, most known system for Standard Definition (SD) are not suitable for real-time High Definition (HD) image processing and low power consuming requirement in IoT. A HD ALPR accelerator for the IoT endpoint System on Chip (SoC) is proposed in this paper to meet the needs of computations. Based on the programming flexibility of IoT endpoint SoC, it can switch between HD and SD resolutions, which can avoid the specific resolution switching algorithm. A Field Programmable Gate Array (FPGA) chip is transplanted the Cortex-M0 as the IoT endpoint SoC, through the design of ALPR accelerator and Cortex-M0, data communication is achieved by First-In, First-Out (FIFO) with AMBA High-performance Bus (AHB) interface. Heterogeneous implementation of ALPR system has shown that this HD ALPR algorithm can recognize a license plate in 12.5ms, with a success rate of 95.5%. The system utilizes 41,763 Look-Up-Tables (LUTs) without special FPGA IP core. The comparison shows that the system proposed in this paper makes performance of the SoC based on the Cortex-M0 kernel was two times higher than the Cortex-A72 SoC and 39% of the power consumption of Zynq-7000 that is typicalheterogeneous ALPR platform.

Application analysis of shale gas core hyperspectral in weican-1 well

Hylogger core scanning system is used to detailed scan shale gas drilling cores, obtaining visible-near infrared (spectral range 380nm-1100nm),short-wave infrared (spectral range 1100nm-2500nm) spectral curves and high-definition core images. Taking Well weican 1 in the West Taikang Uplift in the South China North Basin as an example, based on the analysis of clay mineral types in the core of the target layer, it is judged to be an alkaline deposit environment rich in iron and magnesium with a temperature of about130°C. Combined with mineral information and core image, the core of the target layer is divided,mud shale is developed, the cumulative thickness exceeds 400m, the maximum single layer thickness reaches 38m, at the same time, the hydrocarbon absorption characteristics of the shale section are obvious, it shows a good prospect for shale gas in the region.

Hyperspectral analysis of shale gas drilling in southern Huangling anticline

In this paper, the visible near infrared, short wave red, and thermal infrared spectral data and high-definition core images of three wells in Huangling anticline in Western Hubei are obtained by hylogger. Through comprehensive analysis, it can be seen that the thickness of Niutitang Formation is decreasing from west to East, However, the thickness of Doushantuo Formation is basically the same, ranging from 210m to 220m. Quartz, clay minerals, carbonate and limonite are the main minerals in Niutitang formation, while in Doushantuo Formation dolomite is the main mineral. Dolomite fracture is developed in this area, which is a favorable area for shale gas development.

Semi-Automatic Framework for Traffic Landmark Annotation

We present a semi-automatic annotation method to build a large dataset for traffic landmark detection, where traffic landmarks include traffic signs, traffic lights as well as road markings. Labor-intensive bounding box tagging is a huge challenge to generate a large dataset for detection algorithms. To mitigate the labor, we adopt a high-definition (HD) map and a positioning system. We propose a process to align the HD map and images semi-automatically. Through the registration, the annotations of the HD map can be directly tagged onto traffic landmarks in the images. To make full use of the HD map for the dataset generation, we annotate the traffic landmarks with reference points, following the way that they are represented in the HD map, instead of the bounding boxes. The proposed semi-automatic method speeds up the annotation by a factor of 3.19, as compared to the manual annotation. Our dataset consists of about 150,000 images and includes about 470,000 annotated traffic landmarks. We train a deep neural network on our dataset to detect the traffic landmarks, and its performance is evaluated using a novel evaluation metric. Moreover, we show that the pretrained traffic landmark detection network is effective in detecting traffic landmarks in other countries using the bounding box by fine-tuning.

Research on Sports Video Image Analysis Based on the Fuzzy Clustering Algorithm

Aimed at the shortcomings of the current sports video image segmentation methods, such as rough image segmentation results and high spatial distortion rate, a sports video image segmentation method based on a fuzzy clustering algorithm is proposed. The second‐order fuzzy attribute with normal distribution and gravity value is established by using the time‐domain difference image, and the membership function of the fuzzy attribute is given; then, the time‐domain difference image is fuzzy clustered, and the motion video image segmentation result is obtained by edge detection. Experimental results show that this method has high spatial accuracy, good noise iteration performance, and low spatial distortion rate and can accurately segment complex moving video images and obtain high‐definition images. The application of this video image analysis method will help master the rules of sports technology and the characteristics of healthy people’s sports skills through video image analysis and help improve physical education, national fitness level, and competitive sports level.

Revision of the endemic Afrotropical genus Tetractenion (Hymenoptera, Ichneumonidae) with an identification key to genera of Banchinae for the region.

The Afrotropical banchine fauna comprises 12 genera: Apophua Morley, Atropha Kriechbaumer, Cryptopimpla Taschenberg, Exetastes Gravenhorst, Glyptopimpla Morley, Himertosoma Schmiedeknecht, Lissonota Gravenhorst, Sjostedtiella Szépligeti, Spilopimpla Cameron, Syzeuctus Förster, Tetractenion Seyrig, and Tossinola Viktorov. A well-illustrated revised key to the genera using high definition images is provided, and the endemic Afrotropical genus Tetractenion is revised, previously represented by two described species. Four new species are described: T.ibayaensissp. nov., T.pascalisp. nov., T.pseudoluteasp. nov., and T.roseisp. nov. The first species-level identification key is provided for this rare genus. Based on morphological attributes the hypothesis is presented that the species in this genus are probably nocturnal. All images and online interactive Lucid keys are available at: www.waspweb.org and the associated underlying data is made available as Suppl. materials 1, 2 LIF3 files to this paper for inter-exchange with other key production software.

Feasibility of Endoscopic Screening for Upper Gastrointestinal Malignancy in a Comprehensive Health Checkup.

Objective In an effort to reduce mortality from gastric cancer, endoscopic screening was introduced in 2016 as a nationwide screening program in Japan. Recent developments in high-definition endoscopic imaging and diagnostic strategies have enabled the simultaneous detection of other upper gastrointestinal (U-GI) malignancies. Therefore, we conducted a study to evaluate the feasibility of endoscopic screening for U-GI malignancy in a comprehensive health checkup. Methods We retrospectively reviewed the data of 13,120 participants who had received a comprehensive health checkup in a single institution between April 2012 and March 2018. Participants were divided into two groups [gastrointestinal endoscopy (GIE) group (n=9,142) and gastrointestinal X-ray (X-ray) group (n=3,978)] and compared with regards to the screening results, adverse events, and detection rate of U-GI malignancies (gastric cancer or other) using a propensity-score matched analysis. Results The gastric cancer detection rate was significantly higher in the GIE group [34/9,142 (0.48%)] than in the X-ray group [3/3,978 (0.08%)] (p=0.003). Other U-GI malignancies were found only in the GIE group and comprised two hypopharyngeal cancers, five esophageal cancers, two duodenal cancers, and one duodenal gastrointestinal stromal tumor. Adverse events occurred in 6/9,142 (0.07%) participants in the GIE group and 18/3,978 (0.45%) participants in the X-ray group (p<0.0001). A propensity-score matched analysis yielded 1,551 matched pairs, and the detection rate of gastric cancer and other U-GI malignancies remained significantly higher in the GIE group than in the X-ray group. Conclusion This study indicated that not only gastric cancer but also other U-GI malignancies can be detected by endoscopic screening.

Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion

This paper presents a new framework for severely distorted image and video recovery via tensor augmentation and completion. By considering the task of representing a matrix by a high-order- $n$ tensor as that of encoding the matrix's 2-D indices $(i,j)$ by $n$ -digit words $i_1i_2\dots i_n$ , we develop a new high order tensor augmentation to cast a third order tensor of color images or video sequences containing missing pixels into a higher order tensor, which is similar to the ket augmentation of quantum physics, is capable of capturing all correlations and entanglements between entries of the original third order tensor. Accordingly, the resuling high-order tensor is completed by our previously developed parallel matrix factorization via tensor train decomposition. Simulations are provided to show the clear advantages of our approach in enhancing important metrics of visual quality such as relative square error and structural similarity index in image and video processing that help to achieve high recovery rates even for high-definition images and videos with 95% missing pixels.

Effective dose and image quality for intraoperative imaging with a cone-beam CT and a mobile multi-slice CT in spinal surgery: A phantom study.

To compare the effective dose (ED) and image quality (IQ) of O-arm cone-beam CT (Medtronic, Minneapolis, MN, USA) and Airo multi-slice CT (Brainlab AG, Munich, Germany) for intraoperative-CT (i-CT) in spinal surgery. The manufacturer-defined protocols available in the O-arm and Airo systems for three-dimensional lumbar spine imaging were compared. Organ dose was measured both with thermo-luminescent dosimeters and GafChromic films in the Alderson RadiationTherapy anthropomorphic phantom. A subjective analysis was performed by neurosurgeons to compare the clinical IQ of the anthropomorphic phantom images acquired with the different i-CT systems and imaging protocols. Image uniformity, noise, contrast-to-noise-ratio (CNR), and spatial resolution were additionally assessed with the Catphan 504 phantom. O-arm i-CT caused 56% larger ED than Airo due to the high definition (HD) imaging protocol. The noise was larger for O-arm images leading to a lower CNR than that measured for Airo. Moreover, scattering and beam hardening effects were observed in the O-arm images. Better spatial resolution was measured for the O-arm system (9 lp/cm) than for Airo (4 lp/cm). For all the investigated protocols, O-arm was found to be better for identifying anatomical features important for accurate pedicle screw positioning. According to phantom measurements, the HD protocol of O-arm offered better clinical IQ than Airo but larger ED. The larger noise of O-arm images did not compromise the clinical IQ while the superior spatial resolution of this system allowed a better visibility of anatomical features important for pedicle screw positioning in the lumbar region.

Laparoscopic Retrograde Biliary Drainage Tube Stenting Technique of Hepaticojejunostomy for Preventing Anastomotic Stenosis of a Small Hepatic Duct: A Case of Choledochal Cyst in a Small Infant

Abstract Background: Laparoscopic hepaticojejunostomy for choledochal cyst in children is one of the most technically challenging operative procedures.1 Recent advances in high-definition imaging m...

Absolute retinal blood flowmeter using a laser Doppler velocimeter combined with adaptive optics.

.Significance: The development of a technique allowing for non-invasive measurement of retinal blood flow (RBF) in humans is needed to understand many retinal vascular diseases (pathophysiology) and evaluate treatment with potential improvement of blood flow.Aim: We developed and validated an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera that provides simultaneously high-definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity to calculate the absolute RBF.Approach: This new absolute LDV is combined with the adaptive optics (AO) fundus camera (rtx1, Imagine Eyes©, Orsay, France) outside its optical wavefront correction path. A 4-s recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides a vessel diameter close to the probing beam, and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. A combination of these values gives an average of the absolute RBF.Results: An in vitro experiment consisting of latex microspheres flowing in water through a glass capillary to simulate a blood vessel and in vivo measurements on six healthy humans was done to assess the device. In the in vitro experiment, the calculated flow varied between 1.75 and and was highly correlated () with the flow imposed by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between and 36% (, ). RBF in the main temporal retinal veins of healthy subjects varied between 0.9 and .Conclusions: The AO LDV prototype allows for the real-time measurement of absolute RBF derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.

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.

Research on Application of Ant Colony Algorithm Based on Multi-sensor Image Fuzzy Fusion in Image Recognition

In the current rapidly developing society, image recognition technology is currently an important means that can provide a full range of dynamic data. It plays an important role in economics, politics and other fields. Fuzzy image fusion algorithms have opened up to solve these problems in image recognition. Based on the current wide application of image recognition, this paper studies the research of multi-sensor image fuzzy fusion algorithm in the application of high-definition image recognition. Aiming at the characteristics of infrared and visible image fusion, an infrared-visible image fusion algorithm based on non-down sampling transformation and hybrid particle swarm algorithm is proposed. For low-frequency sub-images, an improved weighted average method based on area average is used for neighborhood fusion. For high-frequency sub-images, a hybrid particle swarm optimization algorithm is used to select the threshold, and the neighborhood algorithm based on average gradient selection is used for fusion. This algorithm can effectively improve the fusion effect of fused images. The experimental research results show that combining the characteristics of gradient intensity and phase consistency, the characteristic method of compressing the information while retaining important information, which is convenient for real-time processing, is used for related research on sensor image fusion.

Spatial distribution of soil bulk density and its relationship with slope and vegetation allocation model in rehabilitation of dumping site in loess open-pit mine area.

Studies of soil bulk density (BD) spatial variations of land reclaimed after mining have become a focus of land reclamation and ecological restoration research. However, there have been few studies on the relationship among the reconstructed BD, terrain conditions, and vegetation growth. We examined the southern dumping site of the Pingshuo Antaibao open-pit coal mine located in a loess area in China. Field sampling data, digital elevation models (DEMs), and high-definition images were obtained, and indoor testing, geostatistics, and inverse distance weighting (IDW) were applied. This paper aims to analyze the spatial distribution law of the reconstructed BD and focus on its relationship with slope and vegetation allocation models. We demonstrated that (1) BD increased with soil depth and varied moderately within each layer. (2) The BD variation amplitude of the top 0-20-cm soil layer in both the east-west and south-north directions was small and more similar in the east-west direction than in the south-north direction, which was affected by herbaceous root systems. In the next four layers from 20 to 60 cm, the variation in BD in the east-west direction was far larger than that in the south-north direction, which was affected by vegetation classification. (3) On the whole, BD decreased with increasing slope, but when the slope was between 0° and 21°, BD exhibited a specific change law. (4) From the perspective of vegetation classification, the orders of magnitude of BD in the 0-20-cm and 20-60-cm layers differed. Overall, BD in areas vegetated with Korshinsk Peashrub was the lowest, and BD was moderate in areas with mixed vegetation, while BD was the highest in areas without vegetation or only vegetated with Black Locust. The mixed grass-bush-tree or bush-tree mode attained the best effect in regulating BD. These results can improve the basic principles of land reclamation in mining areas and provide a basis for further optimizing land reclamation technology in practice.

Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.

Low prevalence of colorectal neoplasia in microscopic colitis: A large prospective multi-center study

Background and AimsMicroscopic colitis (MC) is the most frequent condition in subjects undergoing ileocolonoscopy for chronic non-bloody diarrhea (CNBD) in Western countries. Emerging evidence has shown a negative association between MC and colorectal cancer. Within this prospective multi-center study we have evaluated the risk of colorectal neoplasia in MC and non-MC patients with CNBD receiving ileocolonoscopy with high-definition plus virtual chromoendoscopic imaging and histopathological assessment. MethodsPatients with CNBD of unknown origin were prospectively enrolled in 5 referral centers in Northern Italy for ileocolonoscopy with high-definition and digital/optical chromoendoscopy plus multiple biopsies in each segment. The prevalence of colorectal neoplasia (cancer, adenoma, serrated lesion) in MC was compared to that observed in a control group including CNBD patients negative for MC, inflammatory bowel disease or eosinophilic colitis. ResultsFrom 2014 and 2017, 546 consecutive CNBD patients were recruited. Among the 492 patients (mean age 53±18 years) fulfilling the inclusion criteria against the exclusion critieria, MC was the predominant diagnosis at histopathological assessment (8.7%: N=43, 28 CC, 15 LC). The regression model adjusted for age and gender showed a significant negative association between the diagnosis of CM and colorectal neoplasia (OR=0.39; 95% CI 0.22−0.67, p <0.001) with a 60% decreased risk of adenomatous and neoplastic serrated polyps as compared to the control group (n=412). ConclusionThis multi-center study confirms MC as a low-risk condition for colorectal neoplasia. No surveillance colonoscopy program is to be performed for MC diagnosis.