Prof. Vitaly Kober Profile

Prof. Vitaly Kober

Professor at CICESE

SPIE Involvement:

Author

Publications (140)

Proceedings Article | 4 October 2023 Poster + Paper

Multiple point cloud registration and global consistency condition

Artyom Makovetskii, Sergei Voronin, Vitaly Kober, Aleksei Voronin, Tatyana Makovetskaya

Proceedings Volume 12674, 126741I (2023) https://doi.org/10.1117/12.2677107

KEYWORDS: Point clouds, 3D modeling, Clouds, Matrices, Databases, Computer simulations, Reconstruction algorithms, Singular value decomposition, Sensors, Mobile robots

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Proceedings Article | 4 October 2023 Poster + Paper

Convolutional auto-encoder to extract local features of 2D images

Vitaly Kober, Sergei Voronin, Artyom Makovetskii, Dmitrii Zhernov, Aleksei Voronin

Proceedings Volume 12674, 126741J (2023) https://doi.org/10.1117/12.2677848

KEYWORDS: Mammography, Neural networks, Feature extraction, Breast cancer, Education and training, Matrices, Image segmentation, Image classification, Classification systems, Imaging systems

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Proceedings Article | 4 October 2023 Poster + Paper

Neural network analog of the ICP algorithm

Sergey Leonov, Alexander Vasilyev, Artyom Makovetskii, Egor Gordionok, Vitaly Kober

Proceedings Volume 12674, 126741H (2023) https://doi.org/10.1117/12.2676383

KEYWORDS: Clouds, Matrices, Neural networks, Evolutionary algorithms, Artificial neural networks, Gallium nitride, Visualization, Point clouds, Analog electronics, Reservoir computing

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The paper deals with the problem of overlapping two point-data clouds. Traditionally, iterative or variational methods are used to solve such problems. However, these methods are ineffective to solve tasks with a large number of points in the clouds or in the cases of task series with real-time cloud mapping. For those tasks, it is more appropriate to use neural network technique and deep learning methods. The overlap of point-data clouds is understood as finding the displacement vector between them and the rotation matrix of the clouds relative to each other. First of all, point-data clouds are reduced to the zero displacement by means of some transformation. To find the rotation matrix for transformed clouds the authors proposed a simple neural network implementation of the ICP algorithm. This implementation consists of two stages substantially formed by neural networks. At the first stage, a two-layer probabilistic network acts as a metric classifier. The first layer of the probabilistic network is composed of radial-basis elements – Gaussians. The Gaussian activation function makes it possible to identify the output of the first layer with the probability showing the proximity of the points of the superimposed clouds. The second layer of this network is competitive. As a result of the probabilistic network, the points of these two clouds are ranked according to the degree of proximity. The point-data clouds sorted by proximity are sent to the second single-layer neural network. On the second stage, the rotation matrix is calculated using the learning procedure according to the Hebb rule. In the case of small point clouds (less than 10 thousand points), it is more appropriate to use a pseudo-inverse rule (calculations using a pseudoinverse Penrose-Moore matrix) based on the Hebb rule. At the output of the second stage, the rotation matrix is obtained, with which we can easily calculate the displacement vector of the original point clouds. The approbation of the proposed point-data cloud overlap method showed a good match on samples from the ModelNet40 database.

Proceedings Article | 4 October 2023 Poster + Paper

Breast abnormalities classification using deep learning feature extraction

Vladislav Pryadka, Andrei Krendal, Jose Gonzalez-Fraga, Vitaly Kober, Anastasia Kober

Proceedings Volume 12674, 126741M (2023) https://doi.org/10.1117/12.2677167

KEYWORDS: Pathology, Mammography, Feature extraction, Image segmentation, Deep learning, Education and training, Breast, Classification systems, Cancer detection, Breast cancer

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Proceedings Article | 4 October 2023 Poster + Paper

Hopping discrete cosine transform

Vitaly Kober, Jose Gonzalez-Fraga, Anastasia Kober

Proceedings Volume 12674, 126741K (2023) https://doi.org/10.1117/12.2677172

KEYWORDS: Windows, Signal processing, Algorithm development, Algorithm testing, Data processing, Picosecond phenomena, Time-frequency analysis, Radar signal processing, Computer simulations, Algorithms

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Proceedings Article | 4 October 2023 Poster + Paper

Deep neural network for incongruent point clouds registration

Sergei Voronin, Alexander Vasilyev, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, Dmitrii Zhernov

Proceedings Volume 12674, 126741L (2023) https://doi.org/10.1117/12.2677782

KEYWORDS: Point clouds, 3D modeling, Clouds, Neural networks, Databases, Education and training, Matrices, Evolutionary algorithms, Network architectures, Singular value decomposition

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Proceedings Article | 3 October 2022 Presentation + Paper

Comparison study of adaptive RGB-D SLAM systems

Alejandro Gonzalez-Sarabia, Jose Gonzalez-Fraga, Vitaly Kober

Proceedings Volume 12226, 122260K (2022) https://doi.org/10.1117/12.2632456

KEYWORDS: Sensors, Motion estimation, 3D modeling, Visualization, Detection and tracking algorithms

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Proceedings Article | 3 October 2022 Poster + Paper

Clustering in coarse registration task and extraction of common parts of point clouds

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin, Mikhail Turov

Proceedings Volume 12226, 122261F (2022) https://doi.org/10.1117/12.2633548

KEYWORDS: Clouds, Matrices, 3D modeling, Data modeling, Reconstruction algorithms, Machine vision, Databases, Computer vision technology, Computer simulations, 3D vision

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Proceedings Article | 3 October 2022 Poster + Paper

ICP error functional using point cloud geometry

Artyom Makovetskii, Sergei Voronin, Vitaly Kober, Aleksei Voronin

Proceedings Volume 12226, 122261G (2022) https://doi.org/10.1117/12.2633559

KEYWORDS: Clouds, Computer simulations, Matrices, Reconstruction algorithms, Sensors, Machine vision, Databases, Computer vision technology, Chemical elements, Vision geometry

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Proceedings Article | 3 October 2022 Poster + Paper

Neural network for 3D point clouds alignment

Sergei Voronin, Alexander Vasilyev, Vitaly Kober, Artyom Makovetskii, Aleksei Voronin, Dmitrii Zhernov

Proceedings Volume 12226, 122261H (2022) https://doi.org/10.1117/12.2633565

KEYWORDS: Clouds, Neural networks, Network architectures, Evolutionary algorithms, Transformers, Feature extraction, Computer simulations, 3D image processing, Matrices, Mathematics

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Proceedings Article | 3 October 2022 Poster + Paper

Breast cancer detection in digital mammography using phase features and machine learning approach

Julia Diaz-Escobar, Vitaly Kober, Arnoldo Díaz-Ramírez

Proceedings Volume 12227, 122270S (2022) https://doi.org/10.1117/12.2632184

KEYWORDS: Digital mammography, Breast cancer, Mammography, Tumor growth modeling, Machine learning, Breast, Tumors, Tissues, Neural networks, Data modeling

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Proceedings Article | 3 October 2022 Poster + Paper

A comparative study of convolutional network models for the classification of abnormalities in mammograms

J. Manuel Valencia-Moreno, J. Alejandro Gonzalez Sarabia, J. Angel González-Fraga, Everardo Gutiérrez-Lopez, Vitaly Kober

Proceedings Volume 12226, 122261I (2022) https://doi.org/10.1117/12.2633799

KEYWORDS: Mammography, Breast cancer, Performance modeling, Cancer, Convolutional neural networks, Databases, Breast

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Proceedings Article | 3 October 2022 Presentation + Paper

Convolutional neural networks for automatic detection of breast pathologies

Jose Gonzalez-Fraga, Vitaly Kober, Everardo Gutierrez-Lopez, J. Alejandro Gonzalez-Sarabia

Proceedings Volume 12226, 122260Z (2022) https://doi.org/10.1117/12.2633449

KEYWORDS: Breast cancer, Mammography, Convolutional neural networks, Image processing

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Proceedings Article | 1 August 2021 Poster + Paper

Regularized variational functional use a rough alignment for point clouds registration

Artyom Makovetskii, Sergei Voronin, Vitaly Kober, Aleksei Voronin

Proceedings Volume 11842, 1184221 (2021) https://doi.org/10.1117/12.2594561

KEYWORDS: Clouds, 3D modeling, Computer simulations, Matrices, Data modeling, Reconstruction algorithms, Mathematics, Databases, Computer science

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Proceedings Article | 1 August 2021 Poster + Paper

ICP algorithm based on stochastic approach

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin

Proceedings Volume 11842, 118421P (2021) https://doi.org/10.1117/12.2594567

KEYWORDS: Clouds, Data modeling, 3D modeling, Stochastic processes, Reconstruction algorithms, Mathematical modeling, Detection and tracking algorithms, Computer simulations, Data processing, Visibility

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Proceedings Article | 21 August 2020 Poster + Paper

Classification of breast abnormalities in digital mammography with a deep convolutional neural network

Alexey Ruchay, Konstantin Dorofeev, Vitaly Kober

Proceedings Volume 11510, 115102D (2020) https://doi.org/10.1117/12.2567252

KEYWORDS: Pathology, Mammography, Image segmentation, Breast, Data modeling, Image classification, Convolutional neural networks, X-rays, Image analysis

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Proceedings Article | 21 August 2020 Poster + Paper

An efficient algorithm of total variation regularization in the two-dimensional case

Artyom Makovetskii, Sergei Voronin, Vitaly Kober, Aleksei Voronin

Proceedings Volume 11510, 115102Y (2020) https://doi.org/10.1117/12.2568954

KEYWORDS: Clouds, Image restoration, Optical filters, Computer simulations, Cameras, Matrices, Image denoising, Denoising, Sensors

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Proceedings Article | 21 August 2020 Paper

Image dehazing based on microscanning approach

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin, Tatyana Makovetskaya

Proceedings Volume 11510, 115102X (2020) https://doi.org/10.1117/12.2568946

KEYWORDS: Image processing, Image restoration, Imaging systems, Image sensors, Computer simulations

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