Gashnikov, Mikhael V.

2023

• 1 Gashnikov M.V. Video Codec Using Machine Learning Based on Parametric Orthogonal Filters // Optical Memory and Neural Networks (Information Optics) 2023. — Vol. 32. Issue 4. № 4. — P. 226-232
• 2 Gashnikov M.V. Machine Learning for Multiscale Video Coding // Optical Memory and Neural Networks (Information Optics) 2023. — Vol. 32. Issue 3. № 3. — P. 189-196
• 3 Chubar M.A., Gashnikov M.V. Deep Contextual Video Compression Based on Machine Learning // 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). — 2023. —
• 4 Yuzkiv R., Gashnikov M. Orthogonalization and Parameterization of Convolutional Kernels in Machine Learning for Image and Video Compression // 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). — 2023. —
• 5 Maksimov A., Gashnikov M. Generalization of Machine Learning-Based Image Compression Methods for Video Compression // 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). — 2023. —
• 6 Yakubenko M.A., Gashnikov M.V. Entropy Modeling in Video Compression Based on Machine Learning // 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). — 2023. —

2013

• 1 MYaSNIKOV V.V., GAShNIKOV M.V., Glumov N.I. etc. Regional Geographic Information System for Natural-Gas Distribution Network Monitoring Network Monitoring // The 11-th International Conference on Pattern Recognition And Image Analysis (PRIA-11). — 2013. —

2004

• 1 Chicheva, MA, Gashnikov, MV, Glumov, NI etc. Image compression technology for real-time systems of remote sensing // International Conference on Computing, Communications and Control Technologies, Vol 5, Proceedings. — 2004. — P. 237-241

2005

• 1 Chernov A.V., Chicheva M.A., Gashnikov M.V. etc. Software system for development of algorithms for digital images processing and analysis // Proceedings of the Second IASTED International Multi-Conference on Automation, Control, and Information Technology, ACIT 2005. — 2005. — P. 139-142

2007

• 1 Bavrina A.Yu., Gashnikov M.V., Sergeyev V.V. Compression of 3D digital signals based on hierarchical grid interpolation // Pattern Recognition and Image Analysis 2007. — Vol. 17. Issue 1. — P. 25-27
• 2 Gashnikov M.V., Glumov N.I., Sergeyev V.V. Stabilization of the compressed data formation rate in hierarchical image compression // Pattern Recognition and Image Analysis 2007. — Vol. 17. Issue 1. — P. 79-81
• 3 Gashnikov M.V., Glumov N.I., Myasnikov E.V. etc. Software environment for simulating algorithms for image analysis and processing // Pattern Recognition and Image Analysis 2007. — Vol. 17. Issue 2. — P. 279-283

2008

• 1 Bavrina A.Yu., Gashnikov M.V., Glumov N.I. Efficiency analysis of image compression algorithms based on hierarchical and wavelet representations // Pattern Recognition and Image Analysis 2008. — Vol. 18. Issue 4. — P. 598-601
• 2 Gashnikov M.V., Chernov A.V., Chupshev N.V. Normalization of images of moving objects in case of sequential registration of RGB channels // Computer Optics 2008. — Vol. 32. Issue 1. — P. 93-95

2009

• 1 Gashnikov M.V., Chernov A.V., Chupshev N.V. Color correction of vehicle images during the sequential registration of color channels // Pattern Recognition and Image Analysis 2009. — Vol. 19. Issue 1. — P. 106-108
• 2 Gashnikov M.V., Glumov N.I., Chernov A.V. Preparing a common raster coverage for a territory based on hierarchical compressed presentation of orthoimages // Pattern Recognition and Image Analysis 2009. — Vol. 19. Issue 1. — P. 39-42

2010

• 1 GAShNIKOV M.V., MYaSNIKOV V.V., Ivanov A.A. Software system for identification of optoelectronic digital imaging systems and estimation of their quality // Proceedings of 10-th International Conference on Pattern Recognition and image analysis: New Information Technologies. — 2010. —
• 2 Gashnikov M.V., Fedoseev V.A. Method of surveillance devices arrangement on terrain // Proceedings of the IASTED International Conference on Automation, Control, and Information Technology - Information and Communication Technology, ACIT-ICT 2010. — 2010. — P. 233-235
• 3 Gashnikov M.V., Glumov N.I., Sergeev V.V. A hierarchical compression method for space images // Automation and Remote Control 2010. — Vol. 71. Issue 3. — P. 501-513

2011

• 1 Myasnikov V.V., Ivanov A.A., Gashnikov M.V. etc. Computer program for automatic estimation of digital image quality // Pattern Recognition and Image Analysis 2011. — Vol. 21. Issue 3. — P. 415-418

2014

• 1 Gashnikov M.V., Glumov N.I. Hierarchical grid interpolation for hyperspectral image compression // Computer Optics 2014. — Vol. 38. Issue 1. — P. 87-93
• 2 Gashnikov M.V., Glumov N.I. Hierarchical GRID interpolation under hyperspectral images compression // Optical Memory and Neural Networks (Information Optics) 2014. — Vol. 23. Issue 4. — P. 246-253
• 3 Gashnikov M.V., Glumov N.I. Hierarchical compression for hyperspectral image storage // Computer Optics 2014. — Vol. 38. Issue 3. — P. 482-488

2022

• 1 Yuzkiv R., Gashnikov M. Modification of Machine Learning Algorithms for Embedding in Image Compression Methods // 2022 8th International Conference on Information Technology and Nanotechnology, ITNT 2022. — 2022. —
• 2 Gashnikov M.V., Kuznetsov A.V. Detection of Fake Remote-Sensing Data // Optical Memory and Neural Networks (Information Optics) 2022. — Vol. 31. Issue 1. — P. 16-21
• 3 Gashnikov M. General Structure of an Machine Learning Method for Compression of Images // 2022 8th International Conference on Information Technology and Nanotechnology, ITNT 2022. — 2022. —
• 4 Maksimov A., Gashnikov M. Generalization of Machine Learning-Based Compression Method to Hyperspectral Images // 2022 8th International Conference on Information Technology and Nanotechnology, ITNT 2022. — 2022. —
• 5 Gashnikov M. Pyramidal Image Compression Based on Machine Learning // Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022. — 2022. — P. 240-243
• 6 Gashnikov M.V., Kuznetsov A.V. Detection of artificial fragments embedded in remote sensing images by adversarial neural networks // Computer Optics 2022. — Vol. 46. Issue 4. № 4. — P. 643-649
• 7 Gashnikov M.V. Use of Neural Networks and Decision Trees in Compression of 2D and 3D Digital Signals // Optical Memory and Neural Networks (Information Optics) 2022. — Vol. 31. Issue 4. № 4. — P. 379-392
• 8 Gashnikov M. Choosing Machine Learning Methods for Image Compression // 2022 8th International Conference on Information Technology and Nanotechnology, ITNT 2022. — 2022. —
• 9 Gashnikov M.V., Chubar M.A., Yakubenko M.A. Methods and Algorithms for Image Compression Based on Machine Learning // Optoelectronics, Instrumentation and Data Processing 2022. — Vol. 58. Issue 5. № 5. — P. 495-502

2015

• 1 Gashnikov M.V., Glumov N.I., Myasnikov V.V. etc. Regional geographic information systems for gas network monitoring // Pattern Recognition and Image Analysis 2015. — Vol. 25. Issue 3. — P. 418-422
• 2 Gashnikov M.V., Glumov N.I. Hyperspectral images repository using a hierarchical compression // 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2015 - Posters Proceedings. — 2015. — P. 1-4

2016

• 1 Gashnikov M.V. Параметризация нелинейного предсказателя Грехэма при компрессии цифровых изображений // Computer Optics 2016. — Vol. 40. № 2. — P. 225-231
• 2 Gashnikov M.V. Differential image compression based on adaptive prediction // CEUR Workshop Proceedings. — 2016. — Vol. 1638. — P. 320-326
• 3 Gashnikov M.V., Glumov N.I. Development and investigation of a hierarchical compression algorithm for storing hyperspectral images // Optical Memory and Neural Networks (Information Optics) 2016. — Vol. 25. Issue 3. — P. 168-179
• 4 Gashnikov M.V. Interpolation for hyperspectral images compression // CEUR Workshop Proceedings. — 2016. — Vol. 1638. — P. 327-333
• 5 Gashnikov M.V., Glumov N.I., Kuznetsov A.V. etc. Hyperspectral remote sensing data compression and protection // Computer Optics 2016. — Vol. 40. Issue 5. — P. 689-712
• 6 Gashnikov M.V., Glumov N.I. Onboard processing of hyperspectral data in the remote sensing systems based on hierarchical compression // Computer Optics 2016. — Vol. 40. Issue 4. — P. 543-551
• 7 Gashnikov M.V. Parameterization of the nonlinear greham predictor for digital image compression // Computer Optics 2016. — Vol. 40. Issue 2. — P. 225-231
• 8 Glumov N.I., Gashnikov M.V. Hyperspectral image compression for transmission over communication channel // CEUR Workshop Proceedings. — 2016. — Vol. 1638. — P. 334-339

2017

• 1 Gashnikov M.V. DPCM with an adaptive extrapolator for image compression // CEUR Workshop Proceedings. — 2017. — Vol. 1901. — P. 72-77
• 2 Gashnikov M.V. Parameterized adaptive predictor for digital image compression based on the differential pulse code modulation // Proceedings of SPIE - The International Society for Optical Engineering. — 2017. — Vol. 10341.
• 3 Gashnikov M.V. Statistical encoding algorithm for hierarchical image compression // Optical Memory and Neural Networks (Information Optics) 2017. — Vol. 26. Issue 4. — P. 274-279
• 4 Gashnikov M.V. Statistical encoding for image compression based on hierarchical grid interpolation // Computer Optics 2017. — Vol. 41. Issue 6. — P. 905-912
• 5 Gashnikov M.V. A differential image compression method using adaptive parameterized extrapolation // Optical Memory and Neural Networks (Information Optics) 2017. — Vol. 26. Issue 2. — P. 137-144
• 6 Gashnikov M.V., Myasnikov V.V. Detection of informative fragments for image quality assessment // Proceedings of SPIE - The International Society for Optical Engineering. — 2017. — Vol. 10341.
• 7 Gashnikov M.V. Minimizing the entropy of post-interpolation residuals for image compression based on hierarchical grid interpolation // Computer Optics 2017. — Vol. 41. Issue 2. — P. 266-275
• 8 Gashnikov M.V. Minimizing the entropy of quantized post-interpolation residuals for hierarchical image compression // Procedia Engineering. — 2017. — Vol. 201. — P. 196-205

2018

• 1 Gashnikov M. NEDI-based interpolation for hierarchical image compression // Proceedings of SPIE - The International Society for Optical Engineering. — 2018. — Vol. 10806.
• 2 Denisov S.A., Gashnikov M.V. Interpolation for differential and hierarchical compression of multidimensional signals // CEUR Workshop Proceedings. — 2018. — Vol. 2210. — P. 365-371
• 3 Gashnikov M. Optimization of the hierarchical interpolator for image compression // Proceedings of SPIE - The International Society for Optical Engineering. — 2018. — Vol. 10696.
• 4 Maksimov A.I., Gashnikov M.V. Adaptive interpolation of multidimensional signals for differential compression // Computer Optics 2018. — Vol. 42. Issue 4. — P. 679-687
• 5 Gashnikov M.V. Multidimensional signals interpolation based on NEDI for HGI compression // CEUR Workshop Proceedings. — 2018. — Vol. 2210. — P. 47-55
• 6 Gashnikov M.V. Multidimensional signal interpolation based on autocorrelation models for HGI compression // Journal of Physics: Conference Series. — 2018. — Vol. 1096. Issue 1.
• 7 Maksimov A.I., Gashnikov M.V. Parameterization of the nonlinear interpolator invariant to four directions contours for multidimensional digital signals // CEUR Workshop Proceedings. — 2018. — Vol. 2210. — P. 21-28
• 8 Gashnikov M.V. Context Interpolation of Multidimensional Digital Signals in Problem of Compression // Optical Memory and Neural Networks (Information Optics) 2018. — Vol. 27. Issue 3. — P. 183-190
• 9 Gashnikov M.V. Interpolation of Multidimensional Signals Based on Optimization of Entropy of Postinterpolation Remainders // Optical Memory and Neural Networks (Information Optics) 2018. — Vol. 27. Issue 4. — P. 283-291
• 10 Gashnikov M.V. Interpolation based on context modeling for hierarchical compression of multidimensional signals // Computer Optics 2018. — Vol. 42. Issue 3. — P. 468-475
• 11 Gashnikov M.V. Adaptive Autoregressive Interpolation of Multidimensional Signals under Compression Based on Hierarchical Grid Interpolation // Optical Memory and Neural Networks (Information Optics) 2018. — Vol. 27. Issue 2. — P. 132-138
• 12 Gashnikov M., Maksimov A. Parameterized four direction contour-invariant extrapolator for DPCM image compression // Proceedings of SPIE - The International Society for Optical Engineering. — 2018. — Vol. 10806.

2019

• 1 Gashnikov M.V. Reducing dimension of parametric space based on the approximation of components for interpolation of multidimensional signals // Journal of Physics: Conference Series. — 2019. — Vol. 1368. Issue 3.
• 2 Gashnikov M.V. Optimization of the multidimensional signal interpolator in a lower dimensional space // Computer Optics 2019. — Vol. 43. Issue 4. — P. 653-660
• 3 Gashnikov M.V. Parametric Space Dimensionality Reduction in Multidimensional Signal Interpolation // Optical Memory and Neural Networks (Information Optics) 2019. — Vol. 28. Issue 2. — P. 95-100
• 4 Maksimov A., Gashnikov M. Parameter space dimension reduction for multidimensional signals differential pulse-code modulation interpolator // 7th International Symposium on Digital Forensics and Security, ISDFS 2019. — 2019. —
• 5 Gashnikov M.V. Multidimensional Signal Interpolation Based on Factorization and Dimension Reduction of Decision Rules // Optical Memory and Neural Networks (Information Optics) 2019. — Vol. 28. Issue 4. — P. 332-342
• 6 Gashnikov M.V. Interpolation of multidimensional signals using the reduction of the dimension of parametric spaces of decision rules // CEUR Workshop Proceedings. — 2019. — Vol. 2391. — P. 31-40
• 7 Glumov N.I., Gashnikov M.V. Adaptive interpolation of multidimensional signals for compression on board an aircraft // CEUR Workshop Proceedings. — 2019. — Vol. 2391. — P. 97-102
• 8 Gashnikov M. Multidimensional signal interpolation based on parametric space dimension reduction // 7th International Symposium on Digital Forensics and Security, ISDFS 2019. — 2019. —
• 9 Maksimov A.I., Gashnikov M.V. Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression // CEUR Workshop Proceedings. — 2019. — Vol. 2391. — P. 2-30

2020

• 1 Gashnikov M.V., Kuznetsov A.V. Use of Generative Adversarial Networks to Altering Remote Sensing Data // Optical Memory and Neural Networks (Information Optics) 2020. — Vol. 29. Issue 3. — P. 220-227
• 2 Maksimov A., Gashnikov M. Differential method of multidimensional signals compression based on the adapted parameterized interpolation algorithm // CEUR Workshop Proceedings. — 2020. — Vol. 2665. — P. 20-24
• 3 Kuznetsov A., Gashnikov M. Remote Sensing Image Inpainting with Generative Adversarial Networks // 8th International Symposium on Digital Forensics and Security, ISDFS 2020. — 2020. —
• 4 Gashnikov M.V. Optimal Quantization and Adaptive Interpolation in Compression of Multidimensional Signals // Optical Memory and Neural Networks (Information Optics) 2020. — Vol. 29. Issue 2. — P. 133-141
• 5 Gashnikov M.V. Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space // Computer Optics 2020. — Vol. 44. Issue 1. — P. 101-108
• 6 Gashnikov M. Adaptation of parameterized interpolation algorithms of multidimensional signals for hierarchical and interpolation compression methods // CEUR Workshop Proceedings. — 2020. — Vol. 2665. — P. 14-19
• 7 Glumov N., Gashnikov M. Algorithm for optimizing quantization scales by an arbitrary quality measure // CEUR Workshop Proceedings. — 2020. — Vol. 2665. — P. 29-32
• 8 Gashnikov M.V. Parameterized interpolation for fusion of multidimensional signals of various resolutions // Computer Optics 2020. — Vol. 44. Issue 3. — P. 436-440
• 9 Kuznetsov A.V., Gashnikov M.V. Remote sensing data retouching based on image inpainting algorithms in the forgery generation problem // Computer Optics 2020. — Vol. 44. Issue 5. — P. 763-771
• 10 Gashnikov M. Adaptive interpolation for heterogeneous multidimensional signals fusion // Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology. — 2020. —
• 11 Gashnikov M. Decision-Tree-Based Interpolation for Multidimensional Signal Compression // 8th International Symposium on Digital Forensics and Security, ISDFS 2020. — 2020. —

2021

• 1 Gashnikov M. Compressing Images Using Contextual Interpolator Parameterization // Proceedings of ITNT 2021 - 7th IEEE International Conference on Information Technology and Nanotechnology. — 2021. —
• 2 Gashnikov M. Image Coding Based on Efficient Representation of Trivial Regions in Quantization // Proceedings of ITNT 2021 - 7th IEEE International Conference on Information Technology and Nanotechnology. — 2021. —
• 3 Kuznetsov A., Gashnikov M. Investigation of the Influence of the Retouched Region Size on the RS-Images Quality // Proceedings of ITNT 2021 - 7th IEEE International Conference on Information Technology and Nanotechnology. — 2021. —
• 4 Kuznetsov A., Gashnikov M. Increasing the Size of the Camouflage Area for Remote Sensing Images // Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021. — 2021. — P. 312-315
• 5 Gashnikov M. Hierarchical Representation of Plain Areas of Post-Interpolation Residuals for Image Compression // Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021. — 2021. — P. 324-326
• 6 Gashnikov M.V. Pyramidal Image Compression Using the Parameterized NEDI Algorithm // Optical Memory and Neural Networks (Information Optics) 2021. — Vol. 30. Issue 3. — P. 187-193
• 7 Glumov N., Gashnikov M. Quantizer Optimization Based on Uniform Iterative Expansion of Quantization Intervals // Proceedings of ITNT 2021 - 7th IEEE International Conference on Information Technology and Nanotechnology. — 2021. —
• 8 Gashnikov M.V. A Heuristic Algorithm for Arbitrary-criterion Optimization of Quantization Scales // Optical Memory and Neural Networks (Information Optics) 2021. — Vol. 30. Issue 2. — P. 140-145

2000

• 1 Gashnikov M., Glumov N., Sergeyev V. Compression method for real-time systems of remote sensing // Proceedings - International Conference on Pattern Recognition 2000. — Vol. 15. Issue 3. — P. 228-230