Number Of Pixels Research Articles

Longitudinal changes of funduscopic optic disc size, color and cup-to-disc ratio in school children

BackgroundTo investigate the relationship between changes in the optic disc size and color, cup-to-disc (C/D) ratio, and axial elongation in schoolchildren.MethodsA prospective cohort study was performed in 75 right eyes of elementary school students for six years (from 8.5 to 14.5 years old). In the first and last year, all participants underwent optical axial length measurement and color fundus photography. The optic disc color was calculated by dividing the intensity of red by the sum of the intensity of red, green, and blue. The optic disc area was calculated by modifying the number of pixels according to Bennett’s formula. The C/D ratio was calculated by dividing the vertical cup diameter by vertical optic disc diameter. Wilcoxon signed rank test was used to compare these optic disc parameters and axial length in the first and last year.ResultsMean axial length in the last year (24.82 mm) was significantly longer than that in the first year (23.34 mm). Likewise, the mean optic disc size was significantly smaller in the last year (41,946 pixels) than that in the first year (46,144 pixels). The mean optic disc color in the last year (0.49) was significantly more reddish than that in the first year (0.46), while the mean C/D ratio in last year (0.50) was significantly smaller than that in first year (0.52).ConclusionsDuring the period from 8.5 years to 14.5 years of age, both the optic disc size and C/D ratio became smaller, while the color became more red.

Image Processing Analysis of Plant Morphometry with Examples from the Genus Sedum (Crassulaceae).

The complex systematics of the genus Sedum, the difficulties of its classification and the ambiguity of the concrete identification of the taxa brought about the need to implement a measurement system adaptable to field conditions, so as to facilitate the accuracy of data collection, avoiding the etiolation of samples and, therefore, the deterioration of the morphological structures subject to analysis. Thus, our study describes a digitization of the classic method of making measurements using millimeter paper, thus facilitating the subsequent statistical processing of quantifiable values. Depending on the number of pixels in the photos taken and the pixel/millimeter ratio, a variable measurement scale can be created depending on the size of the analyzed taxomes. The method used adds to the classic taxonomy, which is based on the analysis of morphological characteristics to determine the species of these succulent plants. The applicability of our method is shown by means of the example of an analysis performed on the flowers of the native species of the genus Sedum in the territory of Romania.

A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision

Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ± 4%.

Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning

To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Retrospective analysis of OCT images and model comparison. One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study. The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model. Model performance (Dice score) was compared. Uncertainty was calculated using the formula for Shannon Entropy. The output of both Bayesian technique models showed a greater number of pixels with high entropy than the standard model. Dice scores for the Monte Carlo dropout method (0.90, 95% confidence interval 0.87-0.93) and the ensemble method (0.88, 95% confidence interval 0.85-0.91) were significantly higher (P<0.001) than for the traditional model (0.82, 95% confidence interval 0.78-0.86). Quantifying the uncertainty in a prediction of GA may improve trustworthiness of the models and aid clinicians in decision-making. The Bayesian deep learning techniques generated pixel-wise estimates of model uncertainty for segmentation, while also improving model performance compared with traditionally trained deep learning models. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Features of Vascular Endothelial Growth Factor А (Vegf-А) Expression in Atypical Teratoid-Rhabdoid Tumor of the Central Nervous System in Children

Atypical teratoid rhabdoid tumors (AT/RT) are highly malignant neoplasms composed of poorly differentiated germline cells and a variable number of rhabdoid cells that can differentiate into different cell types. A key diagnostic factor for AT/RT is the absence of nuclear expression of the INI1 protein (SMARCB1 gene product) when tested with an anti-INI1 antibody in an immunohistochemical study. Currently, there is no standard treatment for AT/RT, but treatment approaches have become more aggressive and multimodal. In one study, AT/RT was found to be positive for VEGF. The purpose of our study was to examine the expression of VEGF-A in AT/RT in the central nervous system of children. We analyzed biopsy samples from 6 male patients aged 3 months to 8 years (average age 4.5 years). The level of VEGF-A expression was measured by counting the number of brown pixels in the cytoplasm of tumor cells using the PhotoM program in 1 mm2 of tumor tissue. Immunohistochemistry (IHC) revealed varying levels of cytoplasmic staining with an antibody to VEGF-A in 5 out of 6 patients, with the youngest patient (No. 6) showing staining only in endothelial cells. In conclusion, children with AT/RT exhibit different patterns of VEGF-A expression, potentially dependent on the molecular subgroup. Identifying this marker in these highly aggressive tumors could aid in developing new treatment protocols and determining which patients would benefit most from treatment.

Image reconstruction for compressed ultrafast photography based on manifold learning and the alternating direction method of multipliers

Compressed ultrafast photography (CUP) is a high-speed imaging technique with a frame rate of up to ten trillion frames per second (fps) and a sequence depth of hundreds of frames. This technique is a powerful tool for investigating ultrafast processes. However, since the reconstruction process is an ill-posed problem, the image reconstruction will be more difficult with the increase of the number of reconstruction frames and the number of pixels of each reconstruction frame. Recently, various deep-learning-based regularization terms have been used to improve the reconstruction quality of CUP, but most of them require extensive training and are not generalizable. In this paper, we propose a reconstruction algorithm for CUP based on the manifold learning and the alternating direction method of multipliers framework (ML-ADMM), which is an unsupervised learning algorithm. This algorithm improves the reconstruction stability and quality by initializing the iterative process with manifold modeling in embedded space (MMES) and processing the image obtained from each ADMM iterative with a nonlinear modeling based on manifold learning. The numerical simulation and experiment results indicate that most of the spatial details can be recovered and local noise can be eliminated. In addition, a high-spatiotemporal-resolution video sequence can be acquired. Therefore, this method can be applied for CUP with ultrafast imaging applications in the future.

Non-volatile 2D MoS2/black phosphorus heterojunction photodiodes in the near- to mid-infrared region

Cutting-edge mid-wavelength infrared (MWIR) sensing technologies leverage infrared photodetectors, memory units, and computing units to enhance machine vision. Real-time processing and decision-making challenges emerge with the increasing number of intelligent pixels. However, current operations are limited to in-sensor computing capabilities for near-infrared technology, and high-performance MWIR detectors for multi-state switching functions are lacking. Here, we demonstrate a non-volatile MoS2/black phosphorus (BP) heterojunction MWIR photovoltaic detector featuring a semi-floating gate structure design, integrating near- to mid-infrared photodetection, memory and computing (PMC) functionalities. The PMC device exhibits the property of being able to store a stable responsivity, which varies linearly with the stored conductance state. Significantly, device weights (stable responsivity) can be programmed with power consumption as low as 1.8 fJ, and the blackbody peak responsivity can reach 1.68 A/W for the MWIR band. In the simulation of Faster Region with convolution neural network (CNN) based on the FLIR dataset, the PMC hardware responsivity weights can reach 89% mean Average Precision index of the feature extraction network software weights. This MWIR photovoltaic detector, with its versatile functionalities, holds significant promise for applications in advanced infrared object detection and recognition systems.

A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution Neural Network

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimization (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural–network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.

Evaluating TROPOMI δD Column Retrievals With In Situ Airborne Field Campaign Measurements Using Expanded Collocation Criterion

AbstractSatellite observations of column‐averaged water isotopes are relatively new retrieval products that are in need of further in situ evaluation. Such evaluation studies are generally difficult to perform due to the wide mismatch in temporal and spatial scales between the satellite observations based on instantaneous pixel averages during an overpass and airborne in situ measurements ranging up to several hours over a km‐scale. In addition, topography, weather conditions and in particular cloudiness impose severe constraints on an exact collocation between satellite and airborne in situ measurement platforms. Here we present a new method that allows a comparison between in situ measurements and satellite observations of δD on a broader statistical basis. We use regional isotope‐enabled model simulations as intermediate information to identify the area for best comparisons. Applying our methodology to TROPOMI total column δD retrievals for the L‐WAIVE campaign in Annecy, France, during June 2019 increases the number of satellite pixels for comparison despite widespread cloudiness on average by a factor of 20. In addition, the comparison of simulated and observed δD revealed a dependency of the satellite evaluation on the structure of the middle and upper troposphere. We conclude that our method provides a more robust statistic basis for in situ evaluation of δD satellite retrievals. The method will thus be useful in planning and executing forthcoming validation and evaluation campaigns, and can potentially be used for the evaluation of other satellite products.

Pengolahan Citra Berbasis Video Proccesing dengan Metode Frame Difference untuk Deteksi Gerak

This study discusses the detection of motion of objects in video by utilizing the Frame difference method which aims to process video so as to produce Frames on moving objects. The use of mobile cameras produces video data that is used as test data, the test data is processed with the Frame difference method so as to produce a number of Frames on moving objects in order to detect moving objects in the video because the function of this method is a form of video background reduction that is simplified by a number of pixels in the video. This method process is based on the difference between two consecutive frames in the video aimed at finding differences that occur during the detection process. When processed for detection, the absolute value in the pixel is greater than the predetermined threshold value, it will be considered as a moving object, so that the detection results from the motion detection process will form a box object on the moving object. In this study, the test data used used 20 video data samples with descriptions, 10 test data with bright quality (daytime) and 10 unlit test data (night) with the aim of being able to see how much the level of performance accuracy of the Frame difference method. The test results obtained 16 out of 20 test data that were successfully detected correctly (True Positive), there were 2 test data that resulted in a False Positive error, and 2 test data that resulted in a False Negative error. This shows that the Frame difference method can provide a fairly high level of accuracy in detecting moving objects in the video. The percentage level of accuracy with confussion matrix testing has a precission value of 88%, recal 88% and an accuracy value of 80%.

Digital Comparison of Occlusal Vertical Contacts Between Direct Composite and Indirect Cad/Cam Restorations: An in vivo quantitative assessment

Objective: Clinical assessment of the static occlusal vertical contacts for direct composite and indirect Cad/Cam restorations, and evaluation of the clinical experience level's effect on contact registration. Methods: Sixty restorations on maxillary and mandibular molar teeth were investigated. Postgraduate students performed indirect Cad/Cam restorations (Cerasmart, GC Corp., n=20), and both undergraduate and postgraduate students performed direct composite restorations (Ganeial A’chord, GC Corp., n=20 for each). A single supervisor digitally analyzed the occlusal vertical contacts immediately after by using an intraoral scanner (iTero Element-5D, AlignTech) and OrthoCAD software. The tightness of contacts was assessed by counting the pixel numbers in Photoshop CC (Adobe) software. The statistical analyses were performed by Shapiro-Wilk, One-way ANOVA, Tamhane’s T2 test, Kruska-Wallis test, and Dunn tests (p

Strain-Sensor-In-Pixel Technology for Resolution-Sustainable Stretchable Displays.

One of the limitations of stretchable displays is the severe degradation of resolution or the decrease in the number of pixels per unit area when stretched. Hence, we suggest a strain-sensor-in-pixel (S-SIP) system through the adoption of hidden pixels that are activated only during the stretch mode for maintaining the density of on-state pixels. For the S-SIP system, the gate and source electrodes of InGaZnO thin-film transistors (TFTs) in an existing pixel are connected to a resistive strain sensor through the facile and selective deposition of silver nanowires (AgNWs) via electrohydrodynamic-jet-printing. With this approach, the strain sensor integrated TFT functions as a strain-triggered switch, which responds only to stretching along the designated axes by finely tuning the orientation and cycles of AgNW printing. The strain sensor-integrated TFT remains in an off-state when unstretched and switches to an on-state when stretched, exhibiting a large negative gauge factor of -1.1 × 1010 and a superior mechanical stability enduring 6000 cycles, which enables the efficient structure to operate hidden pixels without requiring additional signal processing. Furthermore, the stable operation of the S-SIP in a 5 × 5-pixel array is demonstrated via circuit simulation, implying the outstanding applicability and process compatibility to the conventional active-matrix display backplanes.

Radio Tomography Imaging using Adjacent Criterion Method to determine the Localization Error

Observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the receive signal strength (RSS) measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvements. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for Radio tomography system localization. This work focuses on the design and development of a Radio tomography system for human localization that will employ a transceiver sensor arrangement to increase the number of measurements, without making any changes to the hardware design as well as the number of pixels in the sensing domain. An image reconstruction technique namely, Adjacent Criterion Method (ACM) was proposed to enhance the image spatial resolution. A number of experiments were used to evaluate the performance of the system. The results showed that the proposed technique improves the spatial resolution and exhibits more accurate tomograms

Detail-preserving Joint Image Upsampling

Image operators can be instrumental to computational imaging and photography. However, many of them are computationally intensive. In this article, we propose an effective yet efficient joint upsampling method to accelerate various image operators. We show that edge-preserving filtering can be facilitated with a downsampling-and-upsampling process. Moreover, when the extent of smoothing is mild, the process is detail preserving, i.e., the fine details lost in the low-resolution (LR) images can be accurately restored in the high-resolution (HR) images. Given an HR input and an LR output of an operator, we downsample the HR input and calculate its affinities to the HR input. By applying the affinities to the LR output, we promote its resolution. Due to the strong detail-preserving property, the HR output derived in the previous step may exhibit aliasing artifacts around the salient edges. We further refine it based on the linear relations in a small neighborhood to rid the artifacts. Experiments on various image operators show that our method achieves superior quality over the state-of-the-art joint upsampling methods. Furthermore, the running time of our method is linear to the number of pixels. Our naive implementation derives 1080P images in real time (24 fps) on an NVIDIA GTX 3070 GPU.

Lightweight Image Encryption Using a Novel Chaotic Technique for the Safe Internet of Things

Recently, the field of lightweight cryptography (LWC) has emerged in response to the security needs of low-cost, widely used technology. It is essential to implement an encryption approach with access control to give less complex, more flexible, and safe access to sensitive data. In this work, a novel lightweight chaotic encryption approach with fuzzy access control is presented to encrypt light images in the IoT domain, while maintaining image quality. With the aid of multiplexer modeling and information shift register technology, the algorithm’s design combines random and chaotic mapping approach based on a specific password key with a predetermined number of fuzzy logic shifts on the password key for the image pixels. Further, to extract the private key with complexity and boost defense against attacks, a shift register and logical xor combination is employed. The simulation of the proposed model for AVR microcontroller has been done under MATLAB software and the design of various encryption components has been used to implement lightweight mapping. The proposed system has been evaluated in terms of histogram analysis, adjacent pixel correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, error mean square analysis, information entropy, pixel number change rate, integrated mean change intensity, peak signal-to-noise ratio, and time complexity. Remarkably, the proposed technique has demonstrated high efficiency. The simulation results show that the homogeneity, energy, contrast, NPCR, and UACI criteria have improved by 11.5%, 13.1%, 19%, 0.53%, and 0.12%, respectively, compared to other methods in other articles.

A Multi-Shot Approach for Spatial Resolution Improvement of Multispectral Images from an MSFA Sensor.

Multispectral imaging technology has advanced significantly in recent years, allowing single-sensor cameras with multispectral filter arrays to be used in new scene acquisition applications. Our camera, developed as part of the European CAVIAR project, uses an eight-band MSFA to produce mosaic images that can be decomposed into eight sparse images. These sparse images contain only pixels with similar spectral properties and null pixels. A demosaicing process is then applied to obtain fully defined images. However, this process faces several challenges in rendering fine details, abrupt transitions, and textured regions due to the large number of null pixels in the sparse images. Therefore, we propose a sparse image composition method to overcome these challenges by reducing the number of null pixels in the sparse images. To achieve this, we increase the number of snapshots by simultaneously introducing a spatial displacement of the sensor by one to three pixels on the horizontal and/or vertical axes. The set of snapshots acquired provides a multitude of mosaics representing the same scene with a redistribution of pixels. The sparse images from the different mosaics are added together to get new composite sparse images in which the number of null pixels is reduced. A bilinear demosaicing approach is applied to the composite sparse images to obtain fully defined images. Experimental results on images projected onto the response of our MSFA filter show that our composition method significantly improves image spatial resolution and minimizes reconstruction errors while preserving spectral fidelity.

Night-time light loss during the 2022 Kyiv offensive revealed through VIIRS DNB

ABSTRACT The satellite low-light imagers can detect changes in nighttime lights caused by ground military conflict. The Kyiv Offensive in Kyiv, Ukraine, which lasted less than 40 days during the Russia – Ukraine conflict, began on 24 February 2022. In addition to the monthly light product, we included a daily light product to analyse the number and average intensity of light pixels in the area around Kyiv over time. The changing trends of two parameters are similar. From February to March, the number of light pixels dropped by 65.2%, and average intensity by 24.5%. To account for daily fluctuations, the relative light ratio was introduced, comparing Kyiv to Moscow, Gomel, and Warsaw. This parameter showed a significant decrease in both the number and intensity of light pixels between days 411 and 423 since January 1, 2021, aligning with the conflict’s start. The relative light ratio revealed a 70.6%, 63.5%, and 57.3% drop in light pixel numbers, and a 9.5%, 24.2%, and 48.4% drop in their average intensity for the respective city pairs. However, unusual fluctuations in the reference cities, such as in Gomel during Christmas and in Moscow at the conflict’s onset, present some problems in using the relative light ratio.

47‐5: Analyzing and Enhancing Display Quality in FRC Algorithm

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform.

Modelling of surface brightness fluctuation measurements

Aims. The goal of this work is to scrutinise the surface brightness fluctuation (SBF) calculation methodology. We analysed the SBF derivation procedure, measured the accuracy of the fitted SBF under controlled conditions, retrieved the uncertainty associated with the variability of a system that is inherently stochastic, and studied the SBF reliability under a wide range of conditions. Additionally, we address the possibility of an SBF gradient detection. We also examine the problems related with biased measurements of the SBF and low luminosity sources. All of this information allows us to put forward guidelines to ensure a valid SBF retrieval. Methods. To perform all the experiments described above, we carried out Monte Carlo simulations of mock galaxies as an ideal laboratory. Knowing its underlying properties, we attempted to retrieve SBFs under different conditions. The uncertainty was evaluated through the accuracy, the precision, and the standard deviation of the fitting. Results. We demonstrate how the usual mathematical approximations taken in the SBF theoretical derivation have a negligible impact on the results and how modelling the instrumental noise reduces the uncertainty. We conducted various studies where we varied the size of the mask applied over the image, the surface and fluctuation brightness of the galaxy, its size and profile, its point spread function, and the sky background. It is worth highlighting that we find a strong correlation between having a high number of pixels within the studied mask and retrieving a low uncertainty result. We address how the standard deviation of the fitting underestimates the actual uncertainty of the measurement. Lastly, we find that, when studying SBF gradients, the result is a pixel-weighted average of all the SBFs present within the studied region. Retrieving an SBF gradient requires high-quality data and a sufficient difference in the fluctuation value through the different radii. We show how the SBF uncertainty can be obtained and we present a collection of qualitative recommendations for a safe SBF retrieval. Conclusions. Our main findings are as follows. It is important to model the instrumental noise, rather than fitting it. The target galaxies must be observed under appropriate observational conditions. In a traditional SBF derivation, one should avoid pixels with fluxes lower than ten times the SBF estimate to prevent biased results. The uncertainty associated with the intrinsic variability of the system can be obtained using sets of Monte Carlo mock galaxy simulations. We offer our computational implementation in the form of a simple code designed to estimate the uncertainty of the SBF measurement. This code can be used to predict the quality of future observations or to evaluate the reliability of those already conducted.

Learning robust correlation with foundation model for weakly-supervised few-shot segmentation

Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large number of pixel masks during training is expensive. This paper considers a more challenging scenario, weakly-supervised few-shot segmentation (WS-FSS), which only provides category (i.e. image-level) labels. It requires the model to learn robust support-query information when the generated mask is inaccurate. In this work, we design a Correlation Enhancement Network (CORENet) with foundation model, which utilizes multi-information guidance to learn robust correlation. Specifically, correlation-guided transformer (CGT) utilizes self-supervised ViT tokens to learn robust correlation from both local and global perspectives. From the perspective of semantic categories, the class-guided module (CGM) guides the model to locate valuable correlations through the pre-trained CLIP. Finally, the embedding-guided module (EGM) implicitly guides the model to supplement the inevitable information loss during the correlation learning by the original appearance embedding and finally generates the query mask. Extensive experiments on PASCAL-5i and COCO-20i have shown that CORENet exhibits excellent performance compared to existing methods.