Персона:
Корнилов, Антон Сергеевич

Организационные подразделения

Организационная единица

Институт общей профессиональной подготовки (ИОПП)

Миссией Института является: фундаментальная базовая подготовка студентов, необходимая для получения качественного образования на уровне требований международных стандартов; удовлетворение потребностей обучающихся в интеллектуальном, культурном, нравственном развитии и приобретении ими профессиональных знаний; формирование у студентов мотивации и умения учиться; профессиональная ориентация школьников и студентов в избранной области знаний, формирование способностей и навыков профессионального самоопределения и профессионального саморазвития. Основными целями и задачами Института являются: обеспечение высококачественной (фундаментальной) базовой подготовки студентов бакалавриата и специалитета; поддержка и развитие у студентов стремления к осознанному продолжению обучения в институтах (САЕ и др.) и на факультетах Университета; обеспечение преемственности образовательных программ общего среднего и высшего образования; обеспечение высокого качества довузовской подготовки учащихся Предуниверситария и школ-партнеров НИЯУ МИФИ за счет интеграции основного и дополнительного образования; учебно-методическое руководство общеобразовательными кафедрами Института, осуществляющими подготовку бакалавров и специалистов по социо-гуманитарным, общепрофессиональным и естественнонаучным дисциплинам, обеспечение единства требований к базовой подготовке студентов в рамках крупных научно-образовательных направлений (областей знаний).

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Корнилов

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Антон Сергеевич

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Two-Stage Alignment of FIB-SEM Images of Rock Samples
(2020) Reimers, I.; Safonov, I.; Kornilov, A.; Yakimchuk, I.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич
Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) tomography provides a stack of images that represent serial slices of the sample. These images are displaced relatively to each other, and an alignment procedure is required. Traditional methods for alignment of a 3D image are based on a comparison of two adjacent slices. However, such algorithms are easily confused by anisotropy in the sample structure or even experiment geometry in the case of porous media. This may lead to significant distortions in the pore space geometry, if there are no stable fiducial marks in the frame. In this paper, we propose a new method, which meaningfully extends existing alignment procedures. Our technique allows the correction of random misalignments between slices and, at the same time, preserves the overall geometrical structure of the specimen. We consider displacements produced by existing alignment algorithms as a signal and decompose it into low and high-frequency components. Final transformations exclude slow variations and contain only high frequency variations that represent random shifts that need to be corrected. The proposed algorithm can operate with not only translations but also with arbitrary affine transformations. We demonstrate the performance of our approach on a synthetic dataset and two real FIB-SEM images of natural rock.
Только метаданные
Visualization of quality of 3D tomographic images in construction of digital rock model
(2020) Reimers, I. A.; Safonov, I. V.; Yakimchuk, I. V.; Kornilov, A. S.; Корнилов, Антон Сергеевич
© 2020 National Research Nuclear University. All rights reserved.Various types of tomography are widely employed in oil and gas industry for studying structure of rocks. Using X-ray or FIB-SEM tomography, a 3D model of a core sample is con-structed for mathematical simulations of fluid flow in porous media and evaluation of physi-cal characteristics of rock. Since images have various defects and distortions, there is a prob-lem of selection of a fragment with the best quality from the initial 3D image. At the moment this operation is made manually on the basis of an expert's opinion and takes significant time. In this paper, we investigate applicability of existing non-reference quality metrics for evalua-tion of tomographic images and propose the approach for visualization of spatial change of 3D image quality. The method includes the construction of central cross-section; plotting graphs of quality and similarity measures for each slice over the cross-section; generation of combined heat map of quality of cubic fragments with various size. The proposed approach significantly accelerates and makes less subjective selection of the best region for further simulations in digital rock workflow. The choice of colour scale is considered to facilitate the analysis of graphical information for people with colour vision deficiency.
Только метаданные
Inpainting of Ring Artifacts on Microtomographic Images by 3D CNN
(2020) Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2020 FRUCT.Ring artifacts are inevitable in microtomographic images. In a Digital Rock workflow, such defects might affect the subsequent segmentation and flow simulation. We propose a correction of ring artifacts in reconstructed microtomographic images by inpainting. Our blind inpainting method uses a 3D convolutional network U-net. For the creation of training and validation datasets, we suggest an algorithm for transferring real ring artifacts to an arbitrary place in the undistorted slices of 8 big images of sandstones and sand. The parameters of the deep neural network and loss functions are analyzed. A loss function based on the multi-scale structural similarity index (MS-SSIM) allows to achieve the best performance. The developed solution corrects ring artifacts perfectly from a point of view of visual assessment and outperforms existing inpainting methods according to quality metrics based on MS-SSIM and mean absolute error (MAE).
Только метаданные
Ring artifacts segmentation on microtomographic images by convolutional neural networks
(2020) Safonov, I.; Yakimchuk, I.; Kornilov, A.; Корнилов, Антон Сергеевич
© 2020 IEEE.Ring artifacts in X-ray microtomographic images can lead to errors in the construction of digital twins of rock samples for flow simulation. Previously, we considered an algorithm for detecting ring artifacts by means of matching filtering of image slices in a polar coordinate system. However, that approach is inapplicable for an arbitrary fragment of an image and requires adjustment of parameters from image to image. In this paper, we propose the segmentation method based on convolutional neural network. Two network architectures are considered: SegNet and U-net. To create a big and representative training and validation datasets, we propose an algorithm for transferring ring artifacts detected by the existing approach from one image to another. Our task-specific data augmentation improves outcomes in comparison with conventional augmentation techniques. The trained model successfully segments ring artifacts even for sample images and artifacts that were not in the training set. The developed algorithm is used to assess the quality of microtomographic images and local correction of image regions damaged by ring artifacts.
Только метаданные
Blind quality assessment for slice of microtomographic image
(2019) Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2019 FRUCT.The paper considers a new algorithm for blind quality assessment of a slice of X-ray microtomographic image. We selected the following factors impacting on micro-CT image quality with respect to Digital Rock technology: Smoothness, sharpness, contrast, absence of high-density regions and ring artifacts. We propose algorithms for estimation of partial quality measures for named factors inside Region-of-Interest, that is in area associated with a sample of rock or granular material. Total quality metrics is calculated as a product of these partial measures. Our method for quality assessment provides reasonable outcomes for synthetic and real slices of micro-CT images. We collected experts' judgments about quality of slices. Proposed solution has a high correlation with scores of experts and outperforms existing blind quality metrics. An application of developed method to all slices allows to obtain quality estimation for 3D micro-CT image.
Только метаданные
Selection in a 3D microtomographic image the region with the highest quality
(2019) Safonov, I. V.; Yakimchuk, I. V.; Kornilov, A. S.; Goncharova, A. V.; Корнилов, Антон Сергеевич
© 2019 CEUR-WS. All rights reserved.We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.
Только метаданные
Deep neural networks for ring artifacts segmentation and corrections in fragments of CT images
(2021) Reimers, I.; Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2021 IEEE Computer Society. All rights reserved.Ring artifacts are typical defects of computed tomography (CT) that degrade the quality of a 3D reconstructed image. Existing techniques for a ring reduction have various shortcomings and limitations, in particular, a lot of them are unable to process arbitrary fragments of the image and blur artifact-free regions. We propose an algorithm for ring artifacts segmentation and reduction by deep convolutional neural networks that correct 3D fragments of the CT image by inpainting. We compare 2D and 3D architectures of networks. For the creation of a dataset with a big number of ring artifacts, we propose a procedure that is able to transfer an artifact from one image to an arbitrary place of another image. The appearance of the transferred artifact changes. For ring artifact segmentation and correction in images of sandstones and sand, the proposed networks demonstrate good visual results and outperform existing methods. The proposed technique concentrates on the Digital Rock workflow, but the networks can be adjusted for the processing of other CT images as well.
Только метаданные
Rendering semisynthetic FIB-SEM images of rock samples
(2021) Reimers, I.; Safonov, I.; Kornilov, A.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич
© 2021 Copyright for this paper by its authors.Digital rock analysis is a prospective approach to estimate properties of oil and gas reservoirs. This concept implies constructing a 3D digital twin of a rock sample. Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) allows to obtain a 3D image of a sample at nanoscale. One of the main specific features of FIB-SEM images in case of porous media is pore-back (or shine-through) effect. Since pores are transparent, their back side is visible in the current slice, whereas, in fact, it locates in the following ones. A precise segmentation of pores is a challenging problem. Absence of annotated ground truth complicates fine-tuning the algorithms for processing of FIB-SEM data and prevents successful application of machine-learning-based methods, which require a huge training set. Recently, several synthetic FIB-SEM images based on stochastic structures were created. However, those images strongly differ from images of real samples. We propose fast approaches to render semisynthetic FIB-SEM images, which imply that intensities of voxels of mineral matrix in a milling plane, as well as geometry of pore space, are borrowed from an image of rock sample saturated by epoxy. Intensities of voxels in pores depend on the distance from milling plane to the given voxel along a ray directed at an angle equal to the angle between FIB and SEM columns. The proposed method allows to create very realistic FIB-SEM images of rock samples with precise ground truth. Also, it opens the door for numerical estimation of plenty of algorithms for processing FIB-SEM data.
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An Animated Graphical Abstract for an Image
(2021) Reimers, I. A.; Safonov, I. V.; Kornilov, A. S.; Сафонов, Илья Владимирович; Корнилов, Антон Сергеевич
© 2021, Springer Nature Switzerland AG.The conventional approach used in modern user interfaces for browsing of a big collection of images by viewing their downsampled versions often does not allow to recognise the content and to assess the quality of the images. To cope with this problem, we propose a method for the creation of an animated graphical abstract. Our approach comprises detection of attention zones, selection of the region for visual quality estimation, and generation of animation that simulates the camera tracking-in, tracking-out, and panning between detected zones and the whole scene. The short looped animation shows the whole image, main objects of the scene one by one, as well as an enlarged fragment for estimation quality of the image. We demonstrate our concept for consumer photographs, scanned images of documents, and slices of tomographic images. The attention zones vary by the type of image. Face detection and salient regions are used for photos. The title, subheaders, and pictures are attention zones for document images. The attention zones in slices of tomographic images in the investigation of rock samples are enlarged regions of a material having different characteristics according to visual similarity. We conducted five user studies to prove the effectiveness of the proposed animated graphical abstract in comparison with conventional thumbnails and icons.
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A Review of Watershed Implementations for Segmentation of Volumetric Images
(2022) Yakimchuk, I.; Kornilov, A.; Safonov, I.; Корнилов, Антон Сергеевич; Сафонов, Илья Владимирович
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.