Статьи журнала - Компьютерная оптика
Все статьи: 2629
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.
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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.
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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.
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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.
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Research on an image color restoration method for old art films
Статья научная
The preservation and restoration of old art films have certain practical value. Focusing on the color restoration of old art films, this paper introduced the same mapping loss on the basis of a cycle-consistent generative adversarial network, which enables the algorithm to capture more image details and achieve better transfer results. The old art films Roman Holiday and Tracks in the Snowy Forest were used as experimental data to verify the color restoration effect of the improved cycle-consistent generative adversarial network algorithm. It was found that compared with the generative adversarial network and cycle-consistent generative adversarial network algorithms, the improved cycle-consistent generative adversarial network algorithm was superior. It achieved a peak signal-to-noise ratio of 26.874, a structural similarity index measure of 0.665, a learned perceptual image patch similarity of 0.212, and a Frechet inception distance of 117.652 for Roman Holiday. Moreover, it achieved a peak signal-to-noise ratio of 22.794, a structural similarity index measure of 0.585, a learned perceptual image patch similarity of 0.247, and a Frechet inception distance of 119.265 for Tracks in the Snowy Forest. It also achieved better results in comparison with existing image color restoration methods. The results demonstrate the usability of the improved cycle-consistent generative adversarial network algorithm in color restoration of old art films, which can be applied in practice.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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 %.
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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.
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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.
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Статья научная
Among cluster analysis methods applied to grayscale image segmentation, Arifin's algorithm is particularly notable. This algorithm enables partitioning the original image into a number of clusters ranging from N_max to 1 in linear time, where N_max represents the number of grayscale levels in the original image. Arifin's algorithm incrementally enlarges pixel clusters by combining a "minimum pair" at each iteration within the calculation cycle, characterized by the minimum distance Dist between clusters. The original method calculates Dist by taking the product of interclass and intraclass variances; however, this approach involves cumbersome formulas and lacks a quality assessment for the resulting partitions. This study introduces two modifications to Arifin's algorithm that simplify the calculation of Dist for identifying "minimum pairs" and performing cluster merging sequences, while also enabling evaluation of cluster partition quality. The first modification uses the increment of the total squared error as a distance function for Dist, whereas the second modification employs partial entropy. In the first approach, partition quality is evaluated based on the total squared error or standard deviation, while in the second, it is assessed by the information content. A computer program has been developed to implement the first modification of Arifin's algorithm, incorporating a difference formula to compute partition quality. This program has been tested using standard test images of various sizes, including full-scale aerospace images (three images at 1024×1024 pixels and one at 2050×2050 pixels). The practical significance of the modification, which leverages the increment of the total squared error to create a series of suboptimal piecewise-constant partitions, lies in reducing the number of operations per cycle needed to determine the "minimum pair" among all adjacent clusters, thereby halving processor time.
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Sensor with enhanced performance based on photonic crystal with a defect layer
Статья научная
We propose an improved structure of an optical biosensor based on a photonic crystal with a defect layer, which can detect the concentration of organic contaminants in water by defect mode shift. We investigated 4 types of defective photonic crystals with different arrangements of layers inside the perfect photonic crystals and their impact on the performance of the sensor. The sensitivity and amplitude of defect mode were examined as a function of defect layer thickness. Also, the peculiarities of edge modes in the presence of defect layer were investigated. Finally, we obtained a characteristic equation to determine the wavelengths of defect modes for an arbitrary 1D photonic crystal with an isotropic defect inside.
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Статья научная
The development and use of a service-oriented application for processing and analyzing meteorological data for solving environmental monitoring problems for the Baikal Natural Territory are considered. The application uses the Web Processing Service standard for creating services. This ensures the ability to work with spatio-temporal data which is typically used in environmental monitoring. As part of the research, algorithms for data normalization, detection of individual and contextual anomalies, and correction of missing and anomalous values, were developed. A distinctive feature of the developed algorithms is the use of a machine learning model based on decision trees to analyze data series when detecting missing values, and the analysis of temporal and seasonal patterns when identifying individual and contextual anomalies using specialized Python programming libraries. The application, utilizing web services, represents an effective tool for comprehensive work with meteorological data for environmental monitoring. The application's use in the study of an autonomous energy complex facilitated the selection of its rational structure and operating parameters to meet electricity demand while maintaining ecological sustainability and resource conservation.
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Simulation of a long-haul fiber optic link with a two-mode optical fiber
Статья научная
Modern telecommunication networks approach the capacity crunch, which is associated with the so-called nonlinear Shannon limit. So, the passage to fiber optic links with few-mode optical fibers is considered as an alternative solution of the described problem concerned with high nonlinearity of conventional commercial single-mode optical fibers. Presently, various designs of few-mode optical fibers have been known, with the recently published works presenting experimental results demonstrating their potentialities for long-haul fiber optic links. A lot of models of long-haul fiber optic links with few-mode optical fibers have been developed based on which features of a few-mode optical fiber transport network were numerically simulated. This work presents the results of simulation of a 6000-km long-haul fiber optic link with a two-mode optical fiber and 100-km-per-span Erbium doped fiber optic amplifiers system under 100 Gbps DP-DQPSK data transmission. We studied the use of particular linearly polarized modes and optical vortices for signal transmission. The computation results were compared with the simulation of the same fiber optic link with a single-mode optical fiber under the identical conditions.
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