Статьи журнала - Компьютерная оптика

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Asymmetric apodization for the comma aberrated point spread function

Asymmetric apodization for the comma aberrated point spread function

Reddy Andra Naresh Kumar, Sagar Dasari Karuna, Khonina Svetlana Nikolaevna

Статья научная

This paper deals with the study of light flux distributions in the point spread function formed by an optical system with a one-dimensional aperture under the influence of the coma aberration. The traditional design of an asymmetric optical filter improves the resolution of a diffraction-limited optical imaging system. In this approach we explore the control of monochromatic aberrations through pupil engineering with asymmetric apodization. This technique employs the amplitude and phase apodization for the mitigation of the effects of third-order aberrations on the diffracted image. On introducing the coma wave aberration effect, the central peak intensity in the field of diffraction is a function of the edge strips width and the amplitude apodization parameter of a one-dimensional pupil filter, whereas the magnitude of the reduction of optical side-lobes is a function of the degree of phase apodization at the periphery of the aperture. The analytically computed results are illustrated graphically in terms of point spread function curves under various considerations of the coma aberrations and a different degree of amplitude and phase apodization. Hence, for the optimum values of apodization, the axial resolution has been analyzed using well-defined quality criteria.

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Automatic target recognition algorithm for low-count terahertz images

Automatic target recognition algorithm for low-count terahertz images

Antsiperov Viacheslav Evgenievich

Статья научная

The paper presents the results of developing an algorithm for automatic target recognition in broadband (0.1-10) terahertz images. Due to the physical properties of terahertz radiation and associated hardware, such images have low contrast, low signal-to-noise ratio and low resolution - i.e. all the characteristics of a low-count images. Therefore, standard recognition algorithms designed for conventional images work poorly or are not suitable at all for the problem considered. We have developed a fundamentally different approach based on clustering 2D point clouds in accordance with a set of predefined patterns. As a result, we reduce the problem of target recognition to the problem of maximizing the image data likelihood with respect to the classes of model objects up to the size and position. The resulting recognition algorithm has a structure close to that of the well-known EM algorithm; its formal scheme is at the end of the paper.

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Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network

Wisudawati Lulu Mawaddah, Madenda Sarifuddin, Wibowo Eri Prasetyo, Abdullah Arman Adel

Статья научная

Breast cancer is a leading cause of death in women due to cancer. According to WHO, in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25 - 30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.

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Bragg-fresnel optics and supermirrors

Bragg-fresnel optics and supermirrors

Erko A., Vidal B.

Статья научная

The main principles and some applications of Bragg-Fresnel multilayer optics and X-ray supermirrors are described. An elliptical Bragg-Fresnel multilayer lens (BFML), designed and fabricated in the IMT RAS has been used for 2-dimensional focusing of the white X-ray synchrotron beam. For the beam energy of about 12 KeV the spot size checked with the knife edge method was about 1 mm. Applications of BFML and supermirrors in x-ray imaging are discussed.

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Building detection by local region features in SAR images

Building detection by local region features in SAR images

Ye Shi Ping, Chen Chao Xiang, Nedzved Alexander, Jiang Jun

Статья научная

The buildings are very complex for detection on SAR images, where the basic features of those are shadows. There are many different representations for SAR shadow. As result it is no possible to use convolutional neural network for building detection directly. In this article we give property analysis of SAR shadows of different type buildings. After that, each region (ROI) prepared for training of building detection is corrected with its own SAR shadow properties. Reconstructions of ROI will be put in a modified YOLO network for building detection with better quality result.

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Calculation of effective mode field area of photonic crystal fiber with digital image processing algorithm

Calculation of effective mode field area of photonic crystal fiber with digital image processing algorithm

Tan Yili, Wang Honglian, Wang Yourong

Статья научная

Photonic crystal fiber as a new type of optical fiber has been extensively applied because of its unique properties. The effective mode area of optical fiber is an important parameter, which has a great influence on the performance of optical fiber. In this study, digital image processing algo-rithm was used for preprocessing to improve the accuracy of calculation of mode field area. Then the effective mode field area of optical fiber was calculated using Matlab based Gauss fitting method. Take single-mode fiber G.652 as an example, the effective mode field area was calculated using the traditional algorithm and digital image processing algorithm respectively. It was found that the results obtained using digital image processing algorithm were within the allowed error range, suggesting the effectiveness of the algorithm. Then the calculation of the effective mode area of the triangular lattice photonic crystal fiber further verified the reliability of the algorithm.

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Camera parameters estimation from pose detections

Camera parameters estimation from pose detections

Shalimova Ekaterina Alekseevna, Shalnov Evgeny Vadimovich, Konushin Anton Sergeevich

Статья научная

Some computer vision tasks become easier with known camera calibration. We propose a method for camera focal length, location and orientation estimation by observing human poses in the scene. Weak requirements to the observed scene make the method applicable to a wide range of scenarios. Our evaluation shows that even being trained only on synthetic dataset, the proposed method outperforms known solution. Our experiments show that using only human poses as the input also allows the proposed method to calibrate dynamic visual sensors.

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Central Russia heavy metal contamination model based on satellite imagery and machine learning

Central Russia heavy metal contamination model based on satellite imagery and machine learning

Uzhinskiy Alexander Vladimirovich, Vergel Konstantin Nikolaevich

Статья научная

Atmospheric heavy metal contamination is a real threat to human health. In this work, we examined several models trained on in situ data and indices got from satellite images. During 2018-2019, 281 samples of naturally growing mosses were collected in the Vladimir, Yaroslavl, and Moscow regions in Russia. The samples were analyzed using Neutron Activation Analysis to get the contamination levels of 18 heavy metals. The Google Earth Engine platform was used to calculate indices from satellite images that represent summarized information about sampling sites. Statistical and neural models were trained on in situ data and the indices. We focused on the classification task with 8 levels of contamination and used balancing techniques to extend the training data. Three approaches were tested: variations of gradient boosting, multilayer perceptron, and Siamese networks. All these approaches produced results with minute differences, making it difficult to judge which one is better in terms of accuracy and graphical outputs. Promising results were shown for 9 heavy metals with an overall accuracy exceeding 89 %. Al, Fe, and Sb contamination was predicted for 3,000 and 12,100 grid nodes on a 500 km2 area in the Central Russia region for 2019 and 2020. The results, methods, and perspectives of the adopted approach of using satellite data together with machine learning for HM contamination prediction are presented.

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Coherent field phase retrieval using a phase Zernike filter

Coherent field phase retrieval using a phase Zernike filter

Kotlyar Victor Victorovich, Khonina Svetlana Nikolaevna, Soifer Victor Alexandrovich, Wang Yangtiang, Zhao Datzu

Статья научная

Aberrations of the coherent wavefront are analyzed using a phase Zernike filter. Developed iterative methods allow us to design a filter that decomposes the analyzed light field into a set of diffraction orders with amplitudes proportional to the circular Zernike polynomials. We also apply the algorithm to the calculation of the light field phase from measurements of the modules of decomposition coefficients. Operation of a 25-channel filter is simulated.

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Comparative analysis of reflection symmetry detection methods in binary raster images with skeletal and contour representations

Comparative analysis of reflection symmetry detection methods in binary raster images with skeletal and contour representations

Seredin Oleg Sergeevich, Kushnir Olesia Aleksandrovna, Fedotova Sofia Antonovna

Статья научная

The study is a comparative analysis of two fast reflection symmetry axis detection methods: an algorithm to refine the symmetry axis found with a chain of skeletal primitives and a boundary method based on the Fourier descriptor. We tested the algorithms with binary raster images of plant leaves (FLAVIA database). The symmetry axis detection quality and performance indicate that both methods can be used to solve applied problems. Neither method demonstrated any significant advantage in terms of accuracy or performance. It is advisable to integrate both methods for solving real-life problems.

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Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performance

Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performance

Boori Mukesh Singh, Paringer Rustam Aleksandrovich, Choudhary Komal, Kupriyanov Alexander Victorovich

Статья научная

The main aim of this research work is to compare k-nearest neighbor algorithm(KNN)super-vised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and de-velop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classi-fied map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently avail-able on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes spe-cies level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coeffi-cient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Develop-ment of spectral library for land cover classes is a key component needed to facilitate advance ana-lytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data. The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.

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Computational and experimental studies on SnO2 thin films at various temperatures

Computational and experimental studies on SnO2 thin films at various temperatures

Gurushankar K., Grishina M., Gohulkumar M., Kannan Karthik

Статья научная

Tin oxide (SnO2) thin films was prepared by dip-coating technique at various bath temperatures (313, 333, 353 and 373 K) and annealed at 673 K in this study. And the obtained results were studied and correlated with the computational method. Scanning electron microscopy (SEM) investigation demonstrated that the prepared samples are spherical with agglomeration. The elemental analysis (EDAX) confirms the presence of Sn and O. Further, the SnO2 thin films microstructures are simulated, their thermodynamic and surface properties have been calculated. Micro-Raman spectra were recorded for the prepared samples. Micro-Raman results exhibit the first-order Raman mode E1g (475 cm-1) indicating that the grown SnO2 belongs to the rutile structure. In addition, the envelope method used for studying optical characteristics of the thin films from the transmittance spectra. The semiconducting nature of the films has been noticed from linear I-V characteristics. Furthermore, the electrical conductivity studies suggest that the highest conductivity samples acquire the lowest activation energy and their values are also in the semiconducting range.

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Computer controlling of writing beam in laser microfabrication of diffractive optics

Computer controlling of writing beam in laser microfabrication of diffractive optics

Korolkov V., Shimansky R., Cherkashin V., Denk D.

Статья научная

Laser microfabrication of diffractive optics with continuous relief is based on the direct local action of focused laser radiation on the recording material. Control of writing beam parameters (beam power, spot size, waist position) is one of the main tasks in microfabrication using laser writing systems. Method of the control defines the correspondence between the fabricated microrelief of the diffractive optical element and a designed one. Complexity of this task consists in the necessity to take into account a wide range of factors: laser irradiation noises, non-linear characteristic curve of recording material, finiteness of spot size, influence of power modulation and surrounding on beam energy absorption, influence of beam waist position according to recording layer, dependence of characteristic curve of recording material on beam scanning speed, etc. In the present paper we consider a number of methods for computer controlling of writing beam making it possible to compensate or reduce the influence of these factors and improve the quality of DOE microfabrication. The results of experimental application of the developed methods to circular laser writing systems are discussed.

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Computer generated microwave kinoforms

Computer generated microwave kinoforms

Gallagher N.C., Sweeney D.W.

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

Computer optics and its development

Yang-Xun , Yang-Xiao

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

Conditions of a single-mode rib channel waveguide based on dielectric TiO2/SiO2

Butt Muhammad Ali, Kozlova Elena Sergeevna, Khonina Svetlana Nikolaevna

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

Conforming identification of the fundamental matrix in the image matching problem

Fursov Vladimir Alekseyevich, Gavrilov Andrey Vadimovich, Goshin Yegor Vyacheslavovich, Pugachev Kirill Glebovich

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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Copy move forgery detection using key point localized super pixel based on texture features

Copy move forgery detection using key point localized super pixel based on texture features

Rajalakshmi C., Alex Dr. M. germanux, Balasubramanian Dr. R.

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

Crop growth monitoring through Sentinel and Landsat data based NDVI time-series

Boori Mukesh Singh, Choudhary Komal, Kupriyanov Alexander Victorovich

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

Cross-layer optimization technology for wireless network multimedia video

Xia Wei

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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