Статьи журнала - Компьютерная оптика
Все статьи: 2572
Preliminary results in investigation of diffractive high-efficiency objectives
Статья научная
It has been shown that high-efficiency diffractive objectives are an alternative to their refractive counterparts for applications requiring high precision transformation of monochromatic light (for example in interferometers). A 80 mm diameter prototype (N.A. - 0.158; design wavelength 632.8 nm) has been fabricated by direct laser writing on photoresist. It was manufactured on a polar coordinate laser writing system CLWS-300 that is able to write high precision DOEs up to a diameter of 300 mm. The blazed diffractive structures were written directly into a photoresist layer that was spinned on a high-precision substrate. The fabricated objective has a rms wavefront error of less than л/20 in single pass. The residual errors are predictable using manufacturing data that is recorded during the writing process for each element. This permits to provide each element with calibration data. Measurements of the fabricated DOEs show excellent agreement between the predicted and measured wavefront quality.
Бесплатно
Статья научная
Nematic liquid crystal planar and smoothly-irregular waveguide structures were studied experimentally and by the computer modeling. Two types of optical smoothly-irregular waveguide structures promising for application in telecommunications and control systems are studied by numerical simulation: liquid crystal waveguides and thin film solid generalized waveguide Lune-burg lens. Study of the behavior of these waveguide structures where liquid crystal layer can be used to control the properties of the entire device, of course, promising, especially since such devices are also able to perform various sensory functions when changing some external parameters, accompanied by a change in a number of their properties. It can be of interest to researchers not only in the field of the integrated optics but also in some others areas: nano-photonics, optofluid-ics, telecommunications, and control systems. The dependences of the attenuation coefficient (optical losses) of waveguide modes and the effective sizes (correlation radii) of quasi-stationary irregularities of the liquid-crystal layers on the linear laser radiation polarization and on the presence of pulse-periodic electric field were experimentally observed...
Бесплатно
Статья научная
We present a general-purpose, training-free framework for dimensionality reduction and clustering based on per–sample pseudo–Boolean polynomials (PBP). The method constructs compact, interpreTab. features without model fitting and is evaluated under a standardized protocol that compares PBP to PCA, t-SNE, and UMAP using identical inputs and metrics: clustering alignment (V-measure, Adjusted Rand Index), cluster geometry (Silhouette coefficient, Calinski–Harabasz index, Davies–Bouldin index), and supervised probes (linear separability and boundary complexity (1–NN error)). Across 11 diverse datasets spanning tabular, signal, and ecological domains, PBP leads on linear separability in 5/11 datasets and achieves lower boundary complexity in 2/11 datasets, while remaining competitive on clustering metrics. We report best-performing aggregation and sorting configurations per dataset and provide guidance on when PBP should be preferred for interpreTab. analysis and reproducible evaluation.
Бесплатно
Quality inspection of fertilizer granules using computer vision – a review
Статья научная
This research explores the fusion of computer vision and agricultural quality control. It investigates the efficacy of computer vision algorithms, particularly in image classification and object detection, for non-destructive assessment. These algorithms offer objective, rapid, and error-resistant analysis compared to human inspection. The study provides an extensive overview of using computer vision to evaluate grain and fertilizer granule quality, highlighting granule size’s significance. It assesses prevailing object detection methods, outlining their advantages and drawbacks. The paper identifies the prevailing trend of framing quality inspection as an image classification challenge and suggests future research directions. These involve exploring object detection, image segmentation, or hybrid models to enhance fertilizer granule quality assessment.
Бесплатно
RANSAC-Scaled Depth: A Dual-Teacher Framework for Metric Depth Annotation in Data-Scarce Scenarios
Статья научная
This paper addresses the problem of training metric monocular depth estimation models for specialized domains in the absence of labeled real-world data. We propose a hybrid pseudo-labeling method that combines the predictions of two models: a metric "teacher," trained on synthetic data to obtain the correct scale, and a foundational relative "teacher" for structurally accurate scene geometry and depth. The relative depth map is calibrated via a linear transformation, whose parameters are found using the outlier-robust RANSAC algorithm on a subset of "support" points. Experiments on the KITTI dataset show that the proposed approach improves the quality of the pseudo-labels, reducing the commonly used error metric AbsRel by 21.6 % compared to the baseline method. A compact "student" model trained on these labels demonstrated superiority over the baseline model, achieving a 23.8 % reduction in AbsRel and a 13.8 % reduction in RMSE log. The results confirm that the proposed method significantly improves domain adaptation from general purpose to the specific domain, allowing for the creation of high-precision metric models without the need to collect and annotate volumes of real data.
Бесплатно
Статья научная
In this report we describe a fast 3-color method of the measurement of temperature distributions on a surface of a heated solid using a RGB color camera with a high frame rate (100 images per second). Statistical error the RGB method is not high, and do not exceed around 5.5 % which is surprising taking in to account the number of the measurements at each pixel. Comparison of the results of the temperature measurements on a tungsten plate heated by infra-red laser radiation and conducted with this technique and those obtained with the acousto-optical tunable filter technique demonstrate that error of the temperature measured by 3-color method is only two times as high as that of the tandem acousto-optic filter technique method.
Бесплатно
Recognition of biosignals with nonlinear properties by approximate entropy parameters
Статья научная
More and more attention is being paid to the development of methods for the objective analysis of biosignals for computer medical systems. The search for new non-standard methods is aimed at improving the reliability of diagnostics and expanding the areas of their practical application. In this paper, methods for recognizing biomedical signals by the degree of severity of their nonlinear components are considered. An approach based on the use of approximate entropy closely related to Kolmogorov entropy ( K -entropy) is used. Its parameters can be used to detect dynamic irregularities associated with nonlinear properties of signals. The algorithm for calculating this characteristic is considered in detail. Based on model experiments, its main properties are analyzed. It is shown that the entropy of a finite sequence, calculated in accordance with a multistep procedure, can give an erroneous estimate of the degree of regularity of the signal. A procedure for correcting the approximate entropy is proposed, which expands the area of analysis of this function for estimating nonlinearity. It has been established that the transition to adjusted entropy makes it possible to increase the reliability of the detection of chaotic components. A set of entropy parameters is proposed for constructing recognition procedures. Examples of solving the problems of detecting atrial fibrillation by the parameters of the nonlinearity of the rhythmogram, as well as assessing the depth of anesthesia by the electroencephalogram (EEG) are given. Experiments conducted on real recordings of electrocardiogram (ECG) and EEG signals have shown the high efficiency of the proposed algorithms. The proposed methods and algorithms can be used in the development of systems for monitoring ECG of cardiological patients, as well as monitoring the depth of anesthesia by EEG during surgical operations.
Бесплатно
Renewed empirical formulas of Weibull distribution parameters estimates
Статья научная
The empirical formulas proposed in the literature for estimating the parameters of a two-parameter Weibull distribution, obtained using the equations of the moment method, are considered. It is noted that the formulas used to estimate the shape parameter take the form of various types of dependences on the coefficient of variation of the distribution. By modeling the empirical formulas selected for analysis, a comparative analysis of their errors relative to accurate numerical solutions of the moment method equations was carried out. A renewed empirical formula for the shape parameter is proposed. An approach to estimating the scale parameter is proposed, in which the empirical formula of the latter is reduced to the product of the standard deviation of the distribution by a power function of the coefficient of variation with an exponent equal to – 1.027. The results of applying the updated empirical formulas to numerical data obtained by modeling a random sample from the Weibull distribution are presented. It is shown that the accuracy of the proposed empirical formulas is quite high.
Бесплатно
Статья научная
The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.
Бесплатно
Research on robot motion control and trajectory tracking based on agricultural seeding
Статья научная
With the development of science and technology, agricultural production has been gradually industrialized, and the use of robots instead of humans for seeding is one of the agricultural industrializations. This paper studied the seeding path planning and path tracking algorithms of the seeding robot, carried out experiments, and compared the improved proportion, integral, differential (PID) algorithm with the traditional PID control algorithm. The results demonstrated that both the improved and non-improved control algorithms played a good role in tracking on the straight path, but the improved control algorithm had a better tracking effect on the turning path; the displacement deviation and angle deviation of the tracking trajectory of the improved PID algorithm were reduced faster and more stable than the traditional PID algorithm; the tracking trajectory was shorter and the operation time of the robot was less under the improved PID algorithm than the traditional one.
Бесплатно
Retinal biometric identification using convolutional neural network
Статья научная
Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.
Бесплатно
Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems
Статья научная
Crop growth is one of the most important parameters of a crop and its knowledge before harvest is essential to help farmers, scientists, governments and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate rice crop growth in a single year. Sentinel 2 data provides frequent and consistent information to facilitate coastal monitoring from field scales. The aims of this study were to modify the rice growth vegetation index to improve rice growth phenology in the coastal areas. The rice growth vegetation index 2 is the best vegetation index, compared with 11 vegetation indices, plant height and biomass. The results demonstrate that the coefficient of rice growth vegetation index 2 was 0.83, has the highest correlation with plant height. Rice growth vegetation index 2 is more appropriate for enhancing and obtaining rice phenology information. This study analyses the best spectral vegetation indices for estimating rice growth.
Бесплатно
Статья научная
The use of metal 2D subwavelength structures (SWS) is a promising solution for all those applications where a selective emission from a thermal source is desirable, e.g. photovoltaic and blackbody emission. The investigation of the SWS' photonic bandgap properties is challenging, especially for the infrared and visible spectrum, where the fabrication difficulties have always represented an obstacle. In this paper, the anodization of aluminum films as a self-assembly method for the SWS fabrication is proposed. A rigorous calculation of 2D-SWS of gold having high absorptivity in the visible and low-absorptivity in the NIR, their fabrication by DC-sputtering deposition through anodic porous alumina templates, and their optical and topographic characterization are presented. In this paper, the anodization of aluminum films as a self-assembly method for the SWS fabrication is proposed.
Бесплатно
Road images augmentation with synthetic traffic signs using neural networks
Статья научная
Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector.
Бесплатно
Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP
Статья научная
In various computer vision applications, the moving object detection is an essential step. Principal Component Analysis (PCA) techniques are often used for this purpose. However, the performance of this method is degraded by camera shake, hidden moving objects, dynamic background scenes, and / or fluctuating exposure. Robust Principal Component Analysis (RPCA) is a useful approach for reducing stationary background noise as it can recover low rank matrices. That is, moving object is formed by the low power models and the static background of RPCA. This paper proposes a simple alternative minimization algorithm to fix minor discrepancies in the original Principal Component Pursuit (PCP) or RPCA function. A novel hybrid method of cartoon texture features used as a data matrix for RPCA taking into account low-ranking and rare matrix is presented. A new non-convex function is proposed to better control the low-range properties of the video background. Simulation results demonstrate that the proposed algorithm is capable of giving consistent random estimates and can indeed improve the accuracy of object recognition in comparison with existing methods.
Бесплатно
Статья научная
Scattering potentials of hollow particles with ellipsoid-, cylinder- and parallelepiped-like shapes and adjustable edge sharpness are introduced as a difference of two 3D multi-Gaussian functions with suitable parameters. The far-zone intensity distributions generated on weak scattering from such potentials are shown to depend on the scatterer’s boundary thickness, edge softness as well as on its size relative to the wavelength. Possible extension to potentials formed by nested shells of the same or different types and potentials with semi-hollow center is outlined.
Бесплатно
Screen recapture detection based on color-texture analysis of document boundary regions
Статья научная
This paper examines a presentation attack detection when a document recaptured from a screen is presented instead of the original document. We propose an algorithm based on analyzing a moiré pattern within document boundary regions as a distinctive feature of the recaptured image. It is assumed that the pattern overlapping the document boundaries is a recapture artifact, not a match between document and background textures. To detect such a pattern, we propose an algorithm that employs the result of the fast Hough transform of the document boundary regions with enhanced pattern contrast. The algorithm performance was measured for the open dataset DLC-2021, which contains images of mock documents as originals and their screen recaptures. The precision of the proposed solution was evaluated as 95.4 %, and the recall as 90.5 %.
Бесплатно
Security detection of network intrusion: application of cluster analysis method
Статья научная
In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.
Бесплатно
Статья научная
3D mesh segmentation has become an essential step in many applications in 3D shape analysis. In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation. Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the benchmark proposed by Chen et al. to extract the candidate boundaries of a given 3D-model based on a set of geometric criteria. Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts. Our approach was evaluated using different evaluation metrics. The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.
Бесплатно
