Статьи журнала - Компьютерная оптика
Все статьи: 2553
Computer generated microwave kinoforms
Статья научная
Reflective computer-generated holographic elements are used in a quasi-optical fashion to modify both the phase and polarisation of a high-power coherent microwave beam. Theory and desigh for both one and two component systems are discussed as well as some experimental results.
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Computer optics and its development
Статья научная
The article consider prospects of the future development of the Computer Optics and its interrelation with the computer and optic sciences including the optical softwere and hardwere field, photon computer science.
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Conditions of a single-mode rib channel waveguide based on dielectric TiO2/SiO2
Статья научная
In this paper, we propose conditions for the design of a single-mode rib channel waveguide based on dielectric materials such as titanium dioxide (TiO2) and silicon dioxide (SiO2) for the 0.633-µm visible light. We also design Y-splitter structures, which show high-degree optical confinement and low bend losses at various radii of curvatures. Small radii of curvatures are extremely desirable in integrated photonics as they permit decreasing the dimensions but can also potentially reduce power consumption in the active devices.
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Conforming identification of the fundamental matrix in the image matching problem
Статья научная
The article considers the conforming identification of the fundamental matrix in the image matching problem. The method consists in the division of the initial overdetermined system into lesser dimensional subsystems. On these subsystems, a set of solutions is obtained, from which a subset of the most conforming solutions is defined. Then, on this subset the resulting solution is deduced. Since these subsystems are formed by all possible combinations of rows in the initial system, this method demonstrates high accuracy and stability, although it is computationally complex. A comparison with the methods of least squares, least absolute deviations, and the RANSAC method is drawn.
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Статья научная
Identity document recognition is becoming more and more common in our daily lives. As security measures and document standards improve, the number of documents that need to be recognized is also increasing. So, one of the essential tasks of identity document recognition systems is to identify the document type from thousands of possible variants. However, in many cases, we have supplementary information and can reduce a set of possible types on-the-fly to improve processing speed and quality. In this paper, we discuss ID document recognition with on-the-fly type subset selection. The main challenges in such a system are responding within a limited time and achieving computational and memory efficiency for subset handling. We propose a solution based on a feature-matching approach using binary keypoint descriptors and adjusted multi-index hashing, which uses two new heuristics to ensure a constant number of comparisons for each request. We experimentally evaluate this method on the MIDV-500 and MIDV-2019 datasets and demonstrate that it offers an excellent combination of accuracy, configuration time, and search time compared to commonly used hierarchical clustering, hierarchical navigable small-world graphs, multi-probe locality-sensitive hashing, and straightforward brute-force solutions.
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Constraints for Jaccard index-based rotational symmetry focus position in binary images
Статья научная
This study proposes analytical estimate for the size of a binary raster figure region which is guaranteed to contain the rotational symmetry focus. Focus here is the point a maximum Jaccard index between initial figure and rotated one. The size of the region is determined by the lower estimate of the intersection area during the rotation of the approximating primitives, considering the sizes of the inner and outer parts of the figure relative to the primitive. The smallest circumscribed circle or ellipse and sets of concentric circles and ellipses produced by the principal component analysis were used as the approximating figure. To verify the hypothesis that the size of the region is insignificant compared to the area of the figure, we numerically simulated the proposed method with test image datasets.
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Статья научная
Nowadays, various materials are extensively utilized in various fields. These materials often cause invisible damage with the long-term service of machines. A health monitoring system for materials was presented to eliminate safety hazards as much as possible. This study proposed a fiber optic stress wave sensing system in view of Lamb wave damage imaging to address the limitations in the use of materials in some flaw detection systems. Meanwhile, Lamb waves with small propagation attenuation and long distance in the material were selected as the detection method. Then the fiber optic stress wave sensing system was used to carry out damage imaging. The imaging principle of wavelet packet decomposition was selected to avoid losing low-frequency information. The experiment demonstrated that compared to the original method in view of time correlation coefficient, the method used in this study improved the signal-to-noise ratio of damaged images by 1.0112 dB, higher accuracy, better imaging quality, and more pronounced output images. This research offers a theoretical foundation for the application of fiber optic stress wave sensing systems in the flaw detection, provides practical value for the practical application of Lamb waves, and has positive significance for the application of flaw detection in industry.
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Convolutional Neural Network-Based Low Light Image Enhancement Method
Статья научная
Low-light image augmentation has become increasingly important with the advancement of computer vision technologies in a variety of application settings. However, noise and contrast reduction frequently have an impact on image quality in low-light situations. In this paper, a convolutional neural network-based technique for low-light picture augmentation is put forth. The stability of local binary features under variations in illumination is the study’s initial method of providing directional advice for the enhancement algorithm. Second, the addition of a channel attentiveness mechanism improves the network’s capacity to acquire low-light image features. The proposed model of the study performed better on average in the two dataset tests when compared to the contrast-constrained adaptive histogram equalization algorithm and the bilateral filtering algorithm. Additionally, the recall and DICE coefficient performed better in the tests as well, improving by 16.24 % and 4.98 %, respectively. The proposed method outperformed all others in the picture enhancement studies, according to the experimental findings, proving the validity of this study. The purpose of the study is to offer a reference framework for low-light image enhancing techniques.
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Convolutional Neural Network-Based Low Light Image Enhancement Method
Статья научная
With advances in science and technology, remote sensing images are vital for vegetation monitoring. The use of remote sensing allows for the collection of widespread, multi-temporal data on vegetation, leading to a better comprehension and management of natural resources. In this study, a new remote sensing image recognition model is proposed by combining the filtering algorithm to reconstruct the time series curve, fusing the quadratic difference method and decision tree, and introducing the morphological similarity distance method. The results of the experiment indicate that the normalized vegetation index in towns was consistently higher than the index in bodies of water throughout the year. The normalized index for water was generally close to or below 0. Additionally, the normalized index for forests surpassed that of both water and towns. Although the waveforms for all three were similar, the differences were significant. Notably, the forest normalized index curves had a single peak with a noteworthy duration. The study found that the mapping accuracy for fall plants was highest in 2013 (97.37 %) and lowest in 2014 (80.00 %). Similarly, for spring vegetation, the mapping accuracy was greatest in 2017 (97.96 %) and lowest in 2014, but still favorable at 90.00 %. These results highlight the high advantages of the remote sensing identification method proposed by the study for vegetation identification, which is crucial for natural resource management and environmental protection.
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Copy move forgery detection using key point localized super pixel based on texture features
Статья научная
The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.
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Crop growth monitoring through Sentinel and Landsat data based NDVI time-series
Статья научная
Crop growth monitoring is an important phenomenon for agriculture classification, yield estimation, agriculture field management, improve productivity, irrigation, fertilizer management, sustainable agricultural development, food security and to understand how environment and climate change effect on crops especially in Russia as it has a large and diverse agricultural production. In this study, we assimilated monthly crop phenology from January to December 2018 by using the NDVI time series derived from moderate to high Spatio-temporal resolution Sentinel and Landsat data in cropland field at Samara airport area, Russia. The results support the potential of Sentinel and Landsat data derived NDVI time series for accurate crop phenological monitoring with all crop growth stages such as active tillering, jointing, maturity and harvesting according to crop calendar with reasonable thematic accuracy. This satellite data generated NDVI based work has great potential to provide valuable support for assessing crop growth status and the above-mentioned objectives with sustainable agriculture development.
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Cross-layer optimization technology for wireless network multimedia video
Статья научная
With the development of communication technology, wireless Internet has become more and more popular. The traditional network layered protocols cannot meet the increasingly rich network services, especially video. This paper briefly introduced the cross-layer transmission of video in wireless network and the cross-layer optimization algorithm used for improving video transmission quality and improved the traditional cross-layer algorithm. Then, the two cross-layer algorithms were simulated and analyzed on MATLAB software. The results showed that the packet delivery rate, peak signal to noise ratio and downlink throughput of the improved cross-layer algorithm were significantly higher than those of the traditional cross-layer algorithm under the same signal to interference plus noise ratio of receiving users in wireless network; meanwhile, with the increase of signal to interference plus noise ratio of the receiving user, the packet delivery rate and peak signal to noise ratio of the two algorithms increased, and tended to be stable after some signal to interference plus noise ratio, while the throughput of the two algorithms increased linearly. In the established real wireless network, the package delivery rate, peak signal to noise ratio and throughput of video after application of cross-layer algorithm were significantly improved, and the wireless network applying the improved cross-layer algorithm improved more. In summary, compared with the traditional cross-layer algorithm, the improved cross-layer algorithm can better improve the transmission quality of video in wireless network.
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Статья научная
The work is devoted to an overview of the current state of optoacoustic fiber-optic ultrasonic transducers based on thermoelastic effect and fiber-optic interference receivers, its scope, technologies and materials used, the advantages and disadvantages of different methods and the prospects for the development of the industry.
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Cравнение разных подходов к формированию «идеального» оптического вихря
Статья научная
Получены точные аналитические выражения, описывающие комплексную амплитуду идеального оптического вихря, сформированного тремя разными оптическими элементами: амплитудно-фазовым с пропусканием, пропорциональным функции Бесселя, оптимальным фазовым с пропусканием, равным знаковой функции от функции Бесселя, и вихревым аксиконом. Показано, что интенсивность света на кольце больше для оптимального фазового элемента. Ширина светового кольца, сформированного вихревым аксиконом, примерно в два раза больше, чем ширина двух других колец. Таким образом, оптимальный элемент является наилучшим кандидатом для формирования идеального оптического вихря. Результаты моделирования подтверждают теоретические выводы, а результаты эксперимента согласуются с теорией и результатами моделирования.
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Cуперпозиция оптического вихря и плоской волны с линейными поляризациями в остром фокусе
Статья научная
Рассмотрена острая фокусировка суперпозиции вихревого лазерного пучка с топологическим зарядом n с линейной поляризацией и плоской волны с такой же линейной поляризацией, направленной вдоль горизонтальной оси. В формализме Ричардса–Вольфа получены аналитические выражения для распределения интенсивности и продольной проекции спинового углового момента в плоскости фокуса. Показано, что для четных и нечетных номеров n интенсивность и спиновый угловой момент обладают разной симметрией: при четном n они симметричны относительно обеих декартовых осей, а при нечетном n они симметричны только относительно вертикальной оси. Распределение интенсивности имеет 2n локальных максимумов в фокусе, и на оптической оси интенсивность при любом n отлична от нуля. Распределение продольной проекции спинового углового момента (плотность спина) в плоскости фокуса имеет (n+2) субволновых областей с положительным спиновым угловым моментом и (n+2) областей с отрицательным спиновым угловым моментом, центры которых, чередуясь, лежат на окружности некоторого радиуса с центром на оптической оси. Такое распределение спина с разным знаком демонстрирует продольный спиновый эффект Холла в фокусе. Суммарно в фокусе отрицательный и положительный спин взаимно компенсируется и равен нулю.
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DFW-YOLO: A small insulator target defect detection algorithm based on improved YOLOv8s
Статья научная
With the continuous progress of deep learning technology, UAV aerial photography faces significant challenges for insulator defect detection. Aiming at the problems of low detection accuracy of existing target detection algorithms and difficulty in recognizing small target defects, we propose an improved small target insulator defect detection algorithm based on YOLOv8s, named DFW-YOLO. Firstly, the Detect_Efficient lightweight detection header is proposed using partial convolution (PConv) to lighten the original detection header. Secondly, a FocalModulation focal modulation module is introduced into the backbone network to enhance the model’s extraction and fusion capabilities for features at different scales. Finally, to enhance the model’s focus on poor-quality samples and reduce the harmful gradients they produce, a loss function with a Wise-IoU V3 dynamic non-monotonic focusing mechanism is used instead of the original CIOU loss function. We conducted experiments on a publicly available dataset of UAV aerial photography. According to the experimental data, DFW-YOLO achieves an 86.8% mAP in insulator defect detection, showing a 6.8% improvement compared to the original YOLOv8s model and generally exceeding the performance of other prominent models. Utilizing this method can effectively boost the accuracy of identifying insulator defects in small targets.
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Data mining of corporate financial fraud based on neural network model
Статья научная
Under the active market economy, more and more listed companies emerge. Because of the various interest relationships faced by listed companies, some enterprises which are not well managed or want to enhance company’s value will choose to forge financial reports by improper means. In order to find out the false financial reports as accurately as possible, this paper briefly introduced the relevant indicators for judging the fraudulence of financial reports of listed companies and the recognition model of financial reports based on back propagation (BP) neural network. Then the selection of the input relevant indexes was improved. The improved BP neural network was simulated and analyzed in MATLAB software and compared with the traditional BP neural network and support vector machine (SVM). The results showed that the importance of total assets net profit, earnings per share, cash reinvestment rate, operating gross profit and pre-tax ratio of profit to debt was the top 5 among 20 judgment indexes. In the identification of testing samples of financial report, the accuracy, precision, recall rate and F value all showed that the performance of the improved BP neural network was better than that of the traditional BP network and SVM.
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Статья научная
The purpose of research to create automated personalization of diabetic macular edema laser treatment. The results are based on analysis of large semi-structured data, methods and algorithms for fundus image processing. The technology improves the quality of retina laser coagulation in the treatment of diabetic macular edema, which is one of the main reasons for pronounced vision decrease. The proposed technology includes original solutions to establish an optimal localization of multitude burns by determining zones exposed to laser. It also includes the recognition of large amount of unstructured data on the anatomical and pathological locations' structures in the area of edema and data optical coherent tomography. As a result, a uniform laser application on the pigment epithelium of the affected retina is ensured. It will increase the treatment safety and its effectiveness, thus avoiding the use of more expensive treatment methods. Assessment of retinal lesions volume and quality will allow predicting the laser photocoagulation results and will contribute to the improvement of laser surgeon's skills. The architecture of a software complex comprises a number of modules, including image processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods.
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Статья научная
This paper addresses neural network segmentation of a human olfactory bulb sample on X-ray phase-contrast tomographic reconstruction. The olfactory bulb plays a key role in the primary processing of olfactory information. It consists of several nested cell layers, the morphometric analysis of which has important diagnostic value. However, manual segmentation of the reconstructed volume is labor-intensive and requires high qualifications, which makes the development of automated segmentation methods crucial. X-ray phase-contrast tomography provides a high-resolution reconstruction of the olfactory bulb morphological structure. The resulting reconstructions are characterized by excessive morphological details and reconstruction artifacts. These features, combined with limited data volume, visual similarity of neighboring slices, and sparse ground truth, hinder the application of standard neural network-based segmentation approaches. This paper examines the characteristics of the data under consideration and suggests a training pipeline for a convolutional neural network, including inter-slice smoothing at the data preprocessing stage, alternative strategies for splitting the data into subsets, a set of augmentations, and training on sparse sampling. The proposed adaptations achieved a Dice score (micro) value of 0.93 on the test subset. An ablation study demonstrated that each of the above-mentioned modifications independently improves segmentation quality. The presented training pipeline can be applied to the segmentation of morphological structures on tomographic images in biomedical tasks with a limited dataset and non-standard ground truth.
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