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Статья научная
An original approach to solving difficult time-consuming problems of registration and analysis of random point images is described. The approach is based on the development and application of high-performance specialized computer algebra systems. Three software packages have been created specifically for carrying out equivalent analytical transformations on a computer. The first software system is designed to calculate formulas describing the volumes of convex polyhedra with parametrically specified boundaries in n -dimensional space. The second system is based on the calculation of multidimensional integral expressions by the method of cyclic differentiation of the integral with respect to the parameter. The third system is based on the accelerated implementation of complex combinatorial-recursive transformations on a computer. Another distinctive feature of the work is the extension of the classical Catalan numbers to the multidimensional case (they were required to solve a number of intermediate probabilistic-combinatorial problems). The implementation of the above computer algebra software systems on a multi-core cluster of Novosibirsk State University, together with the direct use of the explicit form of generalized Catalan numbers, allowed the authors to obtain several new previously unknown probabilistic formulas and dependencies required for solving problems in the field of analysis of random point images.
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Статья научная
ID document recognition systems are already deeply integrated into human activity, and the pace of integration is only increasing. The first and most fundamental problems of such systems are document image localization and classification. In this field, template matching-based approaches have become widely used. These methods offer industrial precision, require minimal training data, and provide real-time performance on mobile devices. However, these methods have a significant limitation in scalability: every document type represents a set of local features to store and process, which affects the required computing resources. Moreover, considering the number of different document types supported by modern industrial recognition systems, they become unusable. To mitigate the drawback, we propose a method to select a subset of the most "stable" keypoints. To estimate keypoints' stability we synthesize a dataset of images containing various distortions relevant to the process of taking photos of hand-held documents with a smartphone camera in uncontrolled lighting conditions. To perform experiments we use well-known MIDV datasets, which have been designed to benchmark modern ID document recognition. The experiments show that the proposed method allows for increased ID document detection performance given thousands of document types and with limited computing resources.
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We propose a method of analysis of spontaneous emission of a quantum emitter (an atom, a luminescence center, a quantum dot) inside or in vicinity of a cylinder. At the focus of our method are analytical expressions for the scattering matrix of the cylindrical nanoobject. We propose the approach to electromagnetic field quantization based of eigenvalues and eigenvectors of the scattering matrix. The method is applicable for calculation and analysis of spontaneous emission rates and angular dependences of radiation for a set of different systems: semiconductor nanowires with quantum dots, plasmonic nanowires, cylindrical hollows in dielectrics and metals. Relative simplicity of the method allows obtaining analytical and semi-analytical expressions for both cases of radiation into external medium and into guided modes.
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Статья научная
Beam divergence is one of the instrument resolution parameters in neutron computed tomography. In pinhole geometry, due to the finite size of the source, geometric unsharpness affects the transmission images and therefore influences the reconstructed data. In this paper, we propose an approach for deterministic simulation of this effect for a voxelized 3D object. The idea behind the proposed approach is to use multiple point sources at a pinhole position and collect transmission images from each of them. The implementation was done using the ASTRA toolbox by calculating cone beam projections from each point source. This approach was applied to a porous phantom. Artifacts associated with beam divergence were identified in the reconstructed data. The influence of beam divergence on the segmentation of pores by binarization of the reconstructed data has been considered.
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Modeling the light diffraction by micro-optics elements using the finite element method
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Статья научная
We report a design for creating multilayer dielectric optical filters based on TiO2 and SiO2/MgF2 alternating layers. We have selected Titanium dioxide (TiO2) for high refractive index (2.5), Silicon dioxide (SiO2) and Magnesium fluoride (MgF2) as a low refractive index layer (1.45 and 1.37) respectively. Miniaturized visible spectrometers are useful for quick and mobile characterization of biological samples. Such devices can be fabricated by using Fabry-Perot (FP) filters consisting of two highly reflecting mirrors with a central cavity in between. Distributed Bragg Re-flectors (DBRs) consisting of alternating high and low refractive index material pairs are the most commonly used mirrors in FP filters, due to their high reflectivity. However, DBRs have high re-flectivity for a selected range of wavelengths known as the stopband of the DBR. This range is usually much smaller than the sensitivity range of the spectrometer. Therefore, bandpass filters are required to restrict the wavelength outside the stopband of the FP DBRs. The proposed filter shows high quality with an average transmission of 97 % within the passbands and the transmission outside the passband is around 3 %. Special attention has been given to keep the thickness of the filters within the economic limits. It can be suggested that these filters are exceptionally promising for florescence imaging and narrow-band imaging endoscopy.
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Статья научная
Modified versions of the Wiener filter that replace the mean with the median (MMWF) can better reduce Gaussian noise in images than the classical Wiener filter (WF). However, performance gradually decreases as the noise variance increases. To overcome these limitations, we propose a modified Wiener filter (MADNWF) that replaces the mean with the scaled median absolute deviation (MADN). Similar to MMWF, our modification to the Wiener filter uses a local kernel to average over each pixel. Instead of using the median alone, we replace it with MADN. We used four public datasets for the experiment: Set12, the Tampere17 noise-free dataset, TID2008, and the BSD68 dataset. The first step of this study was to generate 'degraded' images. To achieve this, we added a random amount of zero-mean Gaussian noise with noise variances ranging from 10 to 90, in increments of 10, to every image in the dataset. For comparison with the WF and MMWF, we then applied the proposed MADNWF to reduce noise in degraded images. We evaluated the resulting performance by comparing the reduced noise images with the original images using the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. Experimental results demonstrate that MADNWF consistently outperforms the other methods. At a low noise level (variance = 10), MMWF with a 3x3 filter slightly outperforms in fine-structural preservation, achieving a peak SSIM of 0.6676. However, as the noise intensity increases (variances from 20 to 90), MADNWF achieves complete dominance across all datasets. MADNWF achieves the highest PSNR, up to 33.27 dB at low noise levels, representing an improvement of up to 0.28 dB over WF. Under extreme noise conditions (variance = 90), the MADNWF 7x7 configuration achieves superior image cleanliness (up to 28.91 dB), whereas the MADNWF 5x5 setting serves as the optimal trade-off for structure preservation, outperforming MMWF with an SSIM margin increase of up to 0.0276. In conclusion, our results demonstrate that MADNWF helps reduce the noise distribution in natural images.
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Monitored reconstruction improved by post-processing neural network
Статья научная
Computed tomography (CT) is widely utilized for analyzing internal structures, but the limitations of traditional reconstruction algorithms, which often require a large number of projections, restrict their effectiveness in time-critical tasks or for biological objects studying. Recently Monitored reconstruction approach was proposed for reducing the requirement of dose load. In this paper, there were investigated the advantages of using post-processing neural networks within a monitored reconstruction approach. Three algorithms, namely FBP, FBPConvNet, and LRFR, are evaluated based on their mean count of projections required for the achievement of target reconstruction accuracy. A novel training method specifically designed for neural network algorithms within the Monitored reconstruction framework is proposed. It is shown that the use of the LRFR approach allows one to achieve both a reduction in the number of measured projections and an improvement in the reconstruction accuracy over a certain range of stopping rules. These findings highlight the significant potential of neural networks to be used in the Monitored reconstruction approach.
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Статья научная
In this paper, we demonstrate that combining a laser heating (LH) system with a tandem acousto-optical tunable filter (TAOTF) allows us to measure the temperature distribution (TD) across a laser-heated microscopic specimen. Spectral image processing is based on one-dimensional (1D) non-linear least squares fitting of the Planck radiation function. It is applied for determining the temperature T at each point ( x, y ) of the specimen surface. It is shown that spectral image processing using the 1D non-linear least squares fitting allows measurement of the TD of the laser-heated microscopic specimen with higher precision and stability than those of the conventional linear least-squares fitting of the Wien approximation of Planck’s law.
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Multigrammatical modelling of neural networks
Статья научная
This paper is dedicated to the proposed techniques of modelling artificial neural networks (NNs) by application of the multigrammatical framework. Multigrammatical representations of feed-forward and recurrent NNs are described. Application of multiset metagrammars to modelling deep learning of NNs of the aforementioned classes is considered. Possible developments of the announced approach are discussed.
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Multispectral optoelectronic device for controlling an autonomous mobile platform
Статья научная
The paper substantiates the use of multispectral optoelectronic sensors intended to solve the problem of improving the positioning accuracy of autonomous mobile platforms. A mathematical model of the developed device operation has been suggested in the paper. Its distinctive feature is the cooperative processing of signals obtained from sensors operating in ultraviolet, visible, and infrared ranges and lidar. It reduces the computational complexity of detecting dynamic and stationary objects within the field of view of the device by processing data on the diffuse reflectivity of materials. The paper presents the functional organization of a multispectral optoelectronic device that makes it possible to detect and classify working scene objects with less time spending as compared to analogs. In the course of experimental research, the validity of the mathematical model was evaluated and there were obtained empirical data by means of the proposed hardware and software test stand. The accuracy evaluation of the detected object, at a distance of up to 100m inclusive, is within 0.95. At a distance of more than 100 m, it decreases. This is due to the operating range of a lidar. Error in determining spatial coordinates is of exponential character and it also increases sharply at a distance close to 100 m.
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Mutual modality learning for video action classification
Статья научная
The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same models trained on different modalities (e.g. Optical flow). Unfortunately, it is computationally expensive to use several modalities during inference. Recent works examine the ways to integrate advantages of multi-modality into a single RGB-model. Yet, there is still room for improvement. In this paper, we explore various methods to embed the ensemble power into a single model. We show that proper initialization, as well as mutual modality learning, enhances single-modality models. As a result, we achieve state-of-the-art results in the Something-Something-v2 benchmark.
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Nanophotonic structure formation by dry e-beam etching of the resist: resolution limitation origins
Статья научная
A wide range of structures for nanophotonics and optoelectronics can be formed by dry e-beam etching of the resist (DEBER). High resist sensitivity due to chain depolymerization reaction provides efficient etching with high throughput of the method. The structures obtained by the DEBER in this research are well-rounded diffraction gratings, binary gratings and staircase profiles. The major disadvantage of DEBER is poor lateral resolution, which may be caused by different physical mechanisms. Four groups of possible mechanisms leading to the resolution limitation are determined and the influence of some mechanisms is estimated.
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Статья научная
This paper presents the results of an analysis of the necessary propagation conditions in a step-index optical fiber with a Kerr nonlinearity of two modes, LP01 and LP11 , during the transmission of high-power optical pulses. All results were obtained by solving a system of two nonlinear equations for these modes, obtained by the Gauss approximation method, and the subsequent use of a procedure for refining estimates using the mixed finite elements method. The necessary conditions are determined, estimates of the boundaries for the range of normalised frequencies for which they are fulfilled are obtained, and an approximate formula is proposed for estimating the upper limit of this range.
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Network community partition based on intelligent clustering algorithm
Статья научная
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering algorithm was introduced, and then it was improved based on the node similarity to get better partition results. Finally, experiments were carried out on the random network and the real network. The results showed that the improved clustering algorithm was faster than GN and KL algorithms, the community had larger modularity, and the purity was closer to 1. The experimental results show the effectiveness of the proposed method and make some contributions to the reliable community division.
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Neural network algorithm for optical-SAR image registration based on a uniform grid of points
Статья научная
The paper considers the problem of satellite multimodal image registration, in particular, optical and SAR (Synthetic Aperture Radar). Such algorithms are used in object detection, change detection, navigation. The paper considers algorithms for optical-to-SAR image registration in conditions of rough image pre-alignment. It is known that optical and SAR images have an inaccuracy in registration with georeference (up to 100 pixels with a spatial resolution of 10 m/pixel). This paper presents a neural network algorithm for optical-to-SAR image registration based on descriptors calculated for a uniform grid of points. First, algorithm find uniform grid of points for both images. Next, the neural network calculates descriptors for each point and finds descriptor distances between all possible pairs of points between optical and SAR images. Using obtained descriptor distances, a matching is made between the points on the optical and SAR images. The found matches between points are used to calculate the geometric transformation between images using the RANSAC algorithm with a limited (to combinations of translation, rotation and uniform scaling) affine transformation model. The accuracy of the proposed algorithm for optical-to-SAR image registration was investigated with different distortions in rotation and scale.
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Neural network recognition system for video transmitted through a binary symmetric channel
Статья научная
The demand for transmitting video data is increasing annually, necessitating the use of high-quality equipment for reception and processing. The paper presents a neural network recognition system for videos transmitted via a binary symmetrical channel. The presence of digital noise in the data makes it challenging to recognize objects in videos even with advanced neural networks. The proposed system consists of a noise interference detector, a noise purification system based on an adaptive median filter, and a neural network for recognition. The experiment results demonstrate that the proposed system effectively reduces video noise and accurately identifies multiple objects. This versatility makes the system applicable in various fields such as medicine, life safety, physics, and chemistry. The direction of further research may be to improve the model neural network, increasing the database for training or using other noises for modeling.
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Neural network regularization in the problem of few-view computed tomography
Статья научная
The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.
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Neural network task specialization via domain constraining
Статья научная
This paper introduces a concept of neural network specialization via task-specific domain constraining, aimed at enhancing network performance on data subspace in which the network operates. The study presents experiments on training specialists for image classification and object detection tasks. The results demonstrate that specialization can enhance a generalist's accuracy even without additional data or changing training regimes -- solely by constraining class label space in which the network performs. Theoretical and experimental analyses indicate that effective specialization requires modifying traditional fine-tuning methods and constraining data space to semantically coherent subsets. The specialist extraction phase before tuning the network is proposed for maximal performance gains. We also provide analysis of the evolution of the feature space during specialization. This study paves way to future research for developing more advanced dynamically configurable image analysis systems, where computations depend on the specific input. Additionally, the proposed methods can help improve system performance in scenarios where certain data domains should be excluded from consideration of the generalist network.
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New information technology for phytocenoses regional monitoring using remote sensing data
Статья научная
A new information technology for plant communities monitoring using remote sensing data, oriented for application at the regional level, is proposed. The technology is based on maintaining a base of reference polygons, accumulating data on the boundaries of specific plant communities and related semantic information. This database provides a source of verified and up-to-date information for solving problems of rational nature management. To expand the database of reference polygons, two algorithms for finding new ones are presented: a reliable algorithm (analyzing several growing seasons) and an urgent algorithm (based on the current growing season). The advantage of the proposed system is the integration of data storage, processing, and analysis, which enables the automation of the creation of new and the monitoring of existing polygons, as well as the solution of a wide range of problems based on remote sensing data and artificial intelligence algorithms. Practical tasks in studying of phytocenoses in the Samara Region, implemented using the proposed monitoring technology, are considered: updating forest inventory data, monitoring the status of a rare reintroduced species (Paeonia Tenuifolia), searching for new reference polygons across a vast territory of several steppe protected areas, and searching for areas of presence of an invasive plant species (Elaeagnus angustifolia L).
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