Статьи журнала - Компьютерная оптика
Все статьи: 2511
"Бездифракционные пучки" и их каустики
Статья научная
В настоящей работе обсуждается возможность формирования фокальных областей в различных зонах дифракции и доказывается, что термин "бездифракционные пучки" применяется для обозначения ограниченной в пространстве части тороидальной световой волны. Световая линия, или осевая каустика, является результатом интерференции пересекающихся областей этой волны. Показано, что "бездифракционные пучки" формируются такими оптическими элементами, фазовая функция пропускания которых содержит линейные по радиальной координате слагаемые. Выполнено сравнение оптических характеристик каустик в ближней и в дальней зонах дифракции. Сообщается о синтезе нового дифракционного модулированного аксикона, который формирует две фокальных области различного типа. Приводятся результаты экспериментального исследования его оптических характеристик.
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"Винтовой" пространственный фазовый фильтр
Статья научная
Предложено использовать для фокусировки в кольцо фазовый элемент с пропусканием, фаза которого состоит из двух слагаемых: линейно-радиального слагаемого (аксикона) и линейно-азимутального ("винтового") слагаемого. Получены выражения для максимальной интенсивности света на кольце. Также получены выражения для распределения интенсивности света в зонах дифракции Френеля и Фраунгофера для случая дифракции плоской монохроматической волны на "винтовом" фазовом фильтре.
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"Экзотические" бинарные системы счисления для колец целых чисел Гаусса и Эйзенштейна
Статья научная
В работе рассматриваются нестандартные бинарные системы счисления для колец целых чисел Гаусса и Эйзенштейна. Принципиальным отличием («экзотичностью») таких систем счисления от канонических систем счисления И. Катаи для квадратичных полей является использование в качестве бинарного «цифрового алфавита» двухэлементного множества, не содержащего числового нуля. В работе синтезируются также алгоритмы представления чисел в рассматриваемой системе счисления и характеризуются возможности эффективной реализации арифметических операций.
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3D measurement using fringe projection profilometry
Статья научная
This work is devoted to measuring the depth of the 3D object using the structured light method, in particular, phase shift profilometry. Theoretical studies on the methods of three-dimensional measurement systems and fringe projection profilometry are presented. The phase shift profilometry method with an improved calculation of the frequency of sinusoidal patterns is applied. In practical implementation in the environment (20 cm × 30 cm), the algorithm is tested on a stepped object consisting of eight steps with a difference of 150 mm between two successive steps. In this case, the achievable error for measuring such an object is 20 mm. Our method has great potential in industrial applications where the measurement of the smoothing of the surface of the object is needed to find the defect in the surface with high accuracy without contacting the object.
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3D-reconstruction of the human brain
Статья научная
It is well known that the peculiar manifestations of talent are related to the morphology of human brain. The systematic researched in this field require a visualization of the individual human brain with high accuracy. This method makes it possible to accomplish a reconstruction of human brain from the series of images obtained with the use of equipment of nuclear magnetic resonance. The research of brain morphology is the basis for further visualisation of human activity through the superimposing measurements obtained by means of EEG.
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3D-визуализация многократно рассеивающих сред
Статья научная
Настоящая работа посвящена теоретическим исследованиям распространения низкоинтенсивного лазерного излучения оптического диапазона в многократно рассеивающих средах и направлена на создание универсальной оптической схемы их визуализации. При анализе внутренней структуры объекта и протекающих в нем процессов в большинстве случае необходимо учитывать не абсолютные значения оптических параметров, а их пространственно-временные флуктуации. В связи с этим в качестве базового метода исследования выбран дифференциальный алгоритм метода обратного рассеяния. Построена 3D-модель многократного рассеяния в программной среде TracePro с применением статистического метода Монте-Карло. Проведены численные эксперименты по математическому моделированию взаимодействия лазерного излучения с объектом и визуализации его строения. Определены зависимости дифференциальных характеристик рассеянного излучения от топологии и оптических параметров среды. На основании полученных данных сделан вывод о возможности применения созданной объемной модели для решения задач диагностики многократно рассеивающих объектов, в частности, биообъектов.
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3D-обобщение метода очистки от импульсного шума для обработки видеоданных
Статья научная
В статье предложен обобщенный метод адаптивной медианной фильтрации импульсного шума для обработки видеоданных. Метод основан на совместном применении итеративной обработки и преобразования результата медианной фильтрации на основе распределения Лоренца. Предложены четыре различные комбинации алгоритмических блоков метода. В экспериментальной части статьи приведены результаты сравнения качества работы предложенного метода с известными аналогами. Для моделирования было использовано видео, искаженное импульсным шумом с вероятностями искажения пикселей от 1 % до 99 % включительно. Численная оценка качества очистки видеоданных от шума на основе среднеквадратичной ошибки и индекса структурного сходства показала, что предложенный метод показывает лучший результат обработки во всех рассмотренных случаях по сравнению с известными подходами. Полученные в статье результаты могут найти широкое применение в практических приложениях цифровой обработки видео, например, для обработки визуальных данных в системах видеонаблюдения, идентификации и контроля промышленных процессов.
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A color image encryption algorithm for expert detection system based on composite chaotic sequences
Статья научная
With the development of the Internet, the amount of information carried in images is gradually increasing, and image encryption algorithms for data transmission have been developed. The conventional composite chaotic sequence encryption algorithm has the problem of too long convergence speed when applied to images, which can lead to the risk of information leakage in the image. To address this issue, this study first applies chaotic attractors to improve composite chaotic sequences and enhance the search domain of their Leia Index. At the same time, the Arnold transform technology is introduced into the expert monitoring system, and the two systems are fused to generate a fusion algorithm for color image encryption. Finally, the study conducts experiments on the Differ dataset to verify the effectiveness and superiority of the fusion algorithm, and compares it with three algorithms such as artificial fish schools. The image encryption times of the four algorithms are 6 s, 16 s, 29 s, and 33 s respectively, indicating that the fusion algorithm has the highest encryption speed. When facing exhaustive attacks, the image information damage degrees of the four algorithms are 0.014, 0.051, 0.172, and 0.184, respectively. The experimental results show that the proposed algorithm can effectively resist differential attacks and exhaustive attacks, and is suitable for encrypting color images.
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Статья научная
In this paper, a compact design of a balanced 1×4 optical power splitter based on coupled mode theory (CMT) is presented. The design consists of seven vertically slotted waveguides based on the silicon-on-insulator platform. The 1×4 OPS is modelled using commercial finite element method (FEM) simulation tool COMSOL Multiphysics 5.1. The optimized OPS is capable of working across the whole C-band with maximum ~39 % of power decay in the wavelength range 1530 - 1565 nm.
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Статья научная
Adversarial examples, in the context of computer vision, are inputs deliberately crafted to deceive or mislead artificial neural networks. These examples exploit vulnerabilities in neural networks, resulting in minimal alterations to the original input that are imperceptible by humans but can significantly impact the network’s output. In this paper, we present a thorough survey of research on adversarial examples, with a primary focus on their impact on neural network classifiers. We closely examine the theoretical capabilities and limitations of artificial neural networks. After that, we explore the discovery and evolution of adversarial examples, starting from basic gradient-based techniques and progressing toward the recent trend of employing generative neural networks for this purpose. We discuss the limited effectiveness of existing countermeasures against adversarial examples. Furthermore, we emphasize that the adversarial examples originate the misalignment between human and neural network decision-making processes. That can be attributed to the current methodology for training neural networks. We also argue that the commonly used term “attack on neural networks” is misleading when discussing adversarial deep learning. Through this paper, our objective is to provide a comprehensive overview of adversarial examples and inspire further researchers to develop more robust neural networks. Such networks will align better with human decision-making processes and enhance the security and reliability of computer vision systems in practical applications.
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A framework of reading timestamps for surveillance video
Статья научная
This paper presents a framework to automatically read timestamps for surveillance video. Reading timestamps from surveillance video is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the deep learning framework. The framework has included: training of both timestamp localization and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the timestamps and adapts its resolution to the data. The proposed method achieves state-of-the-art accuracy in the end-to-end timestamps recognition on our datasets, whilst being an order of magnitude faster than competing methods. The framework can be improved the market competitiveness of panoramic video surveillance products.
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Статья
The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of the classical Otsu method is not met. In this work, we considered the imbalanced pixel classes of background and text: weights of two classes are different, but variances are the same. We experimentally demonstrated that the employment of a criterion that takes into account the imbalance of the classes' weights, allows attaining higher binarization accuracy. We described the generalization of the criteria for a two-parametric model, for which an algorithm for the optimal linear separation search via fast linear clustering was proposed. We also demonstrated that the two-parametric model with the proposed separation allows increasing the image binarization accuracy for the documents with a complex background or spots.
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Статья научная
Text recognition has benefited considerably from deep learning research, as well as the preprocessing methods included in its workflow. Identity documents are critical in the field of document analysis and should be thoroughly researched in relation to this workflow. We propose to examine the link between deep learning-based binarization and recognition algorithms for this sort of documents on the MIDV-500 and MIDV-2020 datasets. We provide a series of experiments to illustrate the relation between the quality of the collected images with respect to the binarization results, as well as the influence of its output on final recognition performance. We show that deep learning-based binarization solutions are affected by the capture quality, which implies that they still need significant improvements. We also show that proper binarization results can improve the performance for many recognition methods. Our retrained U-Net-bin outperformed all other binarization methods, and the best result in recognition was obtained by Paddle Paddle OCR v2.
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Статья научная
A developing area of wireless nodal seismic systems installation rises an urgent problem of identification of applicable areas for mounting wireless seismic modules. The identification of applicable areas could be done using geospatial image analysis methods, which require representative datasets that reflect proper features of the surfaces related exactly to the requirements of seismic module installation. This states the problem of development of a methodology for labelling such datasets. This work is devoted to developing methodology for automated labelling of geospatial images using georeferece data from OpenStreetMap that provides accurate vector georeferences of distinct objects, however, suffer from class labels inconsistence (labelling the same object by multiple classes, labelling mistakes, objects overlapping). The distinctive features of the methodology are the development of system of surface classes specific to the properties of applicable surfaces for seismic modules installation and mapping procedure of OSM objects to the developed classification classes based on manual inspection of the OSM objects. The other features of the methodology are data representativeness in terms of geography, obtaining time, as well as maintaining the same lightning conditions. The collected according to the methodology dataset consists of 200 labelled images. The mapping procedure allows avoiding collisions in classes’ labels caused by OSM class hierarchy inconsistency. OSM labels covers 90% of the obtained images.
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A novel approach for partial shape matching and similarity based on data envelopment analysis
Статья научная
Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching.
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A novel switching bilateral filtering algorithm for depth map
Статья научная
In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.
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A parallel fusion method of remote sensing image based on NSCT
Статья научная
Remote sensing image fusion is very important for playing the advantages of a variety of remote sensing data. However, remote sensing image fusion is large in computing capacity and time consuming. In this paper, in order to fuse remote sensing images accurately and quickly, a parallel fusion algorithm of remote sensing image based on NSCT (nonsubsampled contourlet transform) is proposed. In the method, two important kinds of remote sensing image, multispectral image and panchromatic image are used, and the advantages of parallel computing in high performance computing and the advantages of NSCT in information processing are combined. In the method, based on parallel computing, some processes with large amount of calculation including IHS (Intensity, Hue, Saturation) transform, NSCT, inverse NSCT, inverse IHS transform, etc., are done. To realize the method, multispectral image is processed with IHS transform, and the three components, I, H, and S are gotten. The component I and the panchromatic image are decomposed with NSCT...
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Статья научная
Monitoring of land use/cover (LULC) change is very important for sustainable development planning study. This research work is to understand natural and environmental situation and its cause such as intensity, distribution and socio and economic effects in Moscow, Russia based on remote sensing and Geographical Information System techniques. A model was developed by following thematic layers: land use/cover, vegetation, soil, geomorphology and geology in ArcGIS 10.2 software using multi-spectral satellite data obtained from Landsat 7 and 8 for the years of 1995, 2005 and 2016 respectively. Increasing scientific and political interest in regional aspects of global environmental changes, there is a strong stimulus to better understand the patterns causes and environmental consequences of LULC expansion in the elevation of Moscow state, one of the areas in the nation with fast economic growth and high population density. A 70 to 300 m inundation land loss scenarios for surface water and sea level rise (SLR) were developed using digital elevation models of study site topography through remote sensing and GIS techniques by ASTER GDEM and Landsat OLI data. The most severely impacted sectors are expected to be the vegetation, wetland and the natural ecosystem. Improved understanding of the extent and response of SLR will help in preparing for adaptation.
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