Pub Date : 2024-03-20DOI: 10.1134/s1054661823040132
S. A. Dolenko
Abstract
This article is devoted to the history of development and main research areas of the scientific school in the field of pattern recognition, image processing and analysis, and artificial intelligence and machine learning, founded in the early 1990s at the Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University (SINP MSU) by Prof. Igor’ Georgievich Persiantsev. For many years Persiantsev was the permanent leader of this scientific school; he laid down the basic principles and approaches to scientific research that still guide his disciples to this day. During this time, more than 30 people became students of Persiantsev’s school, who carried out scientific work under his leadership or under the leadership of his disciples, defended their candidate’s dissertations or diploma at the Faculty of Physics, Lomonosov Moscow State University. The article provides a brief historical background and an overview of the areas of research and major works published over more than 30 years (from 1992 to 2023) by Persiantsev and his disciples.
{"title":"I.G. Persiantsev’s Scientific School at the Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics: History of Development and Overview of Key Works","authors":"S. A. Dolenko","doi":"10.1134/s1054661823040132","DOIUrl":"https://doi.org/10.1134/s1054661823040132","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article is devoted to the history of development and main research areas of the scientific school in the field of pattern recognition, image processing and analysis, and artificial intelligence and machine learning, founded in the early 1990s at the Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University (SINP MSU) by Prof. Igor’ Georgievich Persiantsev. For many years Persiantsev was the permanent leader of this scientific school; he laid down the basic principles and approaches to scientific research that still guide his disciples to this day. During this time, more than 30 people became students of Persiantsev’s school, who carried out scientific work under his leadership or under the leadership of his disciples, defended their candidate’s dissertations or diploma at the Faculty of Physics, Lomonosov Moscow State University. The article provides a brief historical background and an overview of the areas of research and major works published over more than 30 years (from 1992 to 2023) by Persiantsev and his disciples.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"22 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040454
V. A. Soifer, V. V. Sergeev, V. N. Kopenkov, A. V. Chernov
Abstract
The article examines the role and place of Earth remote sensing (ERS) in geographic information systems. The stages of development of remote sensing and geoinformatics are given, as well as a brief overview of Russian means of obtaining, receiving, and processing satellite images. The specifics and tasks of processing remote sensing data, including hyperspectral data, as well as the experience of using remote sensing data and geoinformation to solve practical problems of managing the territory of the Samara oblast are considered.
{"title":"Earth Remote Sensing and Geographic Information Systems","authors":"V. A. Soifer, V. V. Sergeev, V. N. Kopenkov, A. V. Chernov","doi":"10.1134/s1054661823040454","DOIUrl":"https://doi.org/10.1134/s1054661823040454","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The article examines the role and place of Earth remote sensing (ERS) in geographic information systems. The stages of development of remote sensing and geoinformatics are given, as well as a brief overview of Russian means of obtaining, receiving, and processing satellite images. The specifics and tasks of processing remote sensing data, including hyperspectral data, as well as the experience of using remote sensing data and geoinformation to solve practical problems of managing the territory of the Samara oblast are considered.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040041
L. G. Afraimovich, P. D. Basalin, A. G. Korotchenko, M. Kh. Prilutskii, N. V. Starostin
Abstract
Information about the Nizhny Novgorod scientific and pedagogical school Optimization in Automation Systems for Design and Control is presented. The founder of the school is Honored Scientist of the Russian Federation, Professor Dmitrii Ivanovich Batishchev.
{"title":"Optimization in Automation Systems for Design and Management: Scientific and Pedagogical School of Dmitry Ivanovich Batishchev","authors":"L. G. Afraimovich, P. D. Basalin, A. G. Korotchenko, M. Kh. Prilutskii, N. V. Starostin","doi":"10.1134/s1054661823040041","DOIUrl":"https://doi.org/10.1134/s1054661823040041","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Information about the Nizhny Novgorod scientific and pedagogical school Optimization in Automation Systems for Design and Control is presented. The founder of the school is Honored Scientist of the Russian Federation, Professor Dmitrii Ivanovich Batishchev.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040314
Vl. D. Mazurov, M. I. Poberii, M. Yu. Khachai
Abstract
This article provides an overview of the significant achievements of the Ural School of Pattern Recognition. The focus is on majoritarian generalized solutions for algebraic equations and inequalities that may not always adhere to standard properties. The paper also delves into the broader applications of these findings in collective machine learning techniques. In the literature, these generalized solutions are frequently referred to as committee generalized solutions or simply committees, leading to the derived learning methods being called committee machines. Our discussion primarily centers on the foundational theorems confirming the existence of such solutions, the intricacies of combinatorial optimization during their exploration, and the subsequent emergence of collective machine learning algorithms.
{"title":"Ural School of Pattern Recognition: Majoritarian Approach to Ensemble Learning","authors":"Vl. D. Mazurov, M. I. Poberii, M. Yu. Khachai","doi":"10.1134/s1054661823040314","DOIUrl":"https://doi.org/10.1134/s1054661823040314","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article provides an overview of the significant achievements of the Ural School of Pattern Recognition. The focus is on majoritarian generalized solutions for algebraic equations and inequalities that may not always adhere to standard properties. The paper also delves into the broader applications of these findings in collective machine learning techniques. In the literature, these generalized solutions are frequently referred to as committee generalized solutions or simply committees, leading to the derived learning methods being called committee machines. Our discussion primarily centers on the foundational theorems confirming the existence of such solutions, the intricacies of combinatorial optimization during their exploration, and the subsequent emergence of collective machine learning algorithms.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"109 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s105466182304020x
N. Yu. Ilyasova, N. S. Demin
Abstract
The article is devoted to the achievements of the leading scientific school of Academician V.A. Soifer in the field of biomedical image processing. The main stages of development of research in the field of analysis of medical data are given. Various tasks in processing, analysis, and recognition of medical images, as well as their specifics, are considered. Methods, algorithms, and systems obtained during joint research with major medical institutions in the Russian Federation are described.
{"title":"Systems for Recognition and Intelligent Analysis of Biomedical Images","authors":"N. Yu. Ilyasova, N. S. Demin","doi":"10.1134/s105466182304020x","DOIUrl":"https://doi.org/10.1134/s105466182304020x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The article is devoted to the achievements of the leading scientific school of Academician V.A. Soifer in the field of biomedical image processing. The main stages of development of research in the field of analysis of medical data are given. Various tasks in processing, analysis, and recognition of medical images, as well as their specifics, are considered. Methods, algorithms, and systems obtained during joint research with major medical institutions in the Russian Federation are described.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"230 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s105466182304051x
D. Yu. Vasin
Abstract
This paper contains information about the creation, personnel, and main areas of scientific, scientific-organizational, and educational activities, as well as the main results obtained at the scientific school of the Honored Scientist of the Russian Federation, Doctor of Technical Sciences, Professor Yu.G. Vasin.
{"title":"Scientific School “Models and Methods for Processing Video Information of Spatially Distributed Data, Pattern Recognition, Geoinformation Technologies”","authors":"D. Yu. Vasin","doi":"10.1134/s105466182304051x","DOIUrl":"https://doi.org/10.1134/s105466182304051x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper contains information about the creation, personnel, and main areas of scientific, scientific-organizational, and educational activities, as well as the main results obtained at the scientific school of the Honored Scientist of the Russian Federation, Doctor of Technical Sciences, Professor Yu.G. Vasin.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"16 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040260
V. K. Leontiev
Abstract
A number of meaningful problems commonly associated with pattern recognition is considered. A link between these problems and the branch of science called concept analysis is established.
{"title":"Pattern Recognition and Concept Analysis","authors":"V. K. Leontiev","doi":"10.1134/s1054661823040260","DOIUrl":"https://doi.org/10.1134/s1054661823040260","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A number of meaningful problems commonly associated with pattern recognition is considered. A link between these problems and the branch of science called concept analysis is established.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"30 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040399
V. P. Savelyev, D. Yu. Vasin
Abstract
Information about the Nizhny Novgorod Scientific and Pedagogical School “Dynamics, Mechanics, Control and Mathematical Modeling” is presented. The founder of the school is Honored Scientist of the Russian Federation, Professor Yuri Isaakovich Neimark.
{"title":"Dynamics, Mechanics, Control, and Mathematical Modeling–Scientific and Pedagogical School of Yuri Isaakovich Neimark","authors":"V. P. Savelyev, D. Yu. Vasin","doi":"10.1134/s1054661823040399","DOIUrl":"https://doi.org/10.1134/s1054661823040399","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Information about the Nizhny Novgorod Scientific and Pedagogical School “Dynamics, Mechanics, Control and Mathematical Modeling” is presented. The founder of the school is Honored Scientist of the Russian Federation, Professor Yuri Isaakovich Neimark.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"22 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040429
I. N. Sinitsyn
Abstract
The creation of the theory of canonic expansions (CEs) is related with the names Loeve, Kolmogorov, Karhunen, and Pugachev and dates back to the 1940–1950s. The development of the theory of CEs and wavelet CEs is considered in application to the problems of the analysis, modeling, and synthesis of stochastic systems (SSs) and technologies. The direct and inverse Pugachev theorems about CEs are extended to the case of stochastic linear functionals within the framework of the correlational theory of stochastic functions (SFs). The CEs of linear and quasi-linear SFs are derived. Particular attention is paid to the problems of the equivalent regression linearization of strongly nonlinear transformations by CEs. The nonlinear regression algorithms on the basis of CEs are proposed. The theory of wavelet CEs within the specified domain of the change of the argument on the basis of Haar wavelets is developed. For stochastic elements (SEs), the direct and inverse Pugachev theorems are formulated and the correlational theory of joint CEs for two SEs is developed together with the theory of linear transformations. The solution of linear operator equations by the CEs of SEs in linear spaces with a basis is given. Special attention is focused on the CEs of SEs in Banach spaces with a basis. Some elements of the general theory of distributions for the CEs of SFs and SEs are developed. Particular attention is paid to the method based on CEs with independent components. Some new methods for the calculation of Radon–Nikodym derivatives are proposed. The considered applications of CEs and wavelet CEs to analysis, modeling, and synthesis problems are as follows: SSs and technologies, modeling, identification and recognition filtering, metrological and biometric technologies and systems, and synergic organizational technoeconomic systems (OTESs). The conclusion contains inferences and propositions for further studies. The list of references contains 43 items.
摘要 卡农展开（CE）理论的创立与洛夫（Loeve）、科尔莫戈罗夫（Kolmogorov）、卡尔胡宁（Karhunen）和普加乔夫（Pugachev）等人的名字有关，可追溯到 1940-1950 年代。在应用于随机系统（SS）和技术的分析、建模和综合问题时，考虑了 CE 和小波 CE 理论的发展。在随机函数相关理论（SFs）的框架内，有关 CE 的直接和逆普加乔夫定理被扩展到随机线性函数的情况。推导了线性和准线性 SF 的 CE。特别关注了用 CE 对强非线性变换进行等效回归线性化的问题。提出了基于 CE 的非线性回归算法。以 Haar 小波为基础，发展了参数变化指定域内的小波 CE 理论。对于随机元素（SE），提出了直接和逆普加乔夫定理，并结合线性变换理论发展了两个 SE 的联合 CE 关联理论。给出了在有基础的线性空间中通过 SE 的 CE 求解线性算子方程的方法。特别关注的是带基巴拿赫空间中 SE 的 CE。发展了 SF 和 SE 的 CE 分布一般理论的一些要素。特别关注基于独立分量 CE 的方法。提出了一些计算拉顿-尼科迪姆导数的新方法。所考虑的 CEs 和小波 CEs 在分析、建模和合成问题中的应用如下：SSs 和技术、建模、识别和识别过滤、计量和生物识别技术和系统，以及协同组织技术经济系统 (OTES)。结论包含进一步研究的推论和建议。参考文献清单包含 43 项内容。
{"title":"Developing the Theory of Stochastic Canonic Expansions and Its Applications","authors":"I. N. Sinitsyn","doi":"10.1134/s1054661823040429","DOIUrl":"https://doi.org/10.1134/s1054661823040429","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The creation of the theory of canonic expansions (CEs) is related with the names Loeve, Kolmogorov, Karhunen, and Pugachev and dates back to the 1940–1950s. The development of the theory of CEs and wavelet CEs is considered in application to the problems of the analysis, modeling, and synthesis of stochastic systems (SSs) and technologies. The direct and inverse Pugachev theorems about CEs are extended to the case of stochastic linear functionals within the framework of the correlational theory of stochastic functions (SFs). The CEs of linear and quasi-linear SFs are derived. Particular attention is paid to the problems of the equivalent regression linearization of strongly nonlinear transformations by CEs. The nonlinear regression algorithms on the basis of CEs are proposed. The theory of wavelet CEs within the specified domain of the change of the argument on the basis of Haar wavelets is developed. For stochastic elements (SEs), the direct and inverse Pugachev theorems are formulated and the correlational theory of joint CEs for two SEs is developed together with the theory of linear transformations. The solution of linear operator equations by the CEs of SEs in linear spaces with a basis is given. Special attention is focused on the CEs of SEs in Banach spaces with a basis. Some elements of the general theory of distributions for the CEs of SFs and SEs are developed. Particular attention is paid to the method based on CEs with independent components. Some new methods for the calculation of Radon–Nikodym derivatives are proposed. The considered applications of CEs and wavelet CEs to analysis, modeling, and synthesis problems are as follows: SSs and technologies, modeling, identification and recognition filtering, metrological and biometric technologies and systems, and synergic organizational technoeconomic systems (OTESs). The conclusion contains inferences and propositions for further studies. The list of references contains 43 items.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"37 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.1134/s1054661823040508
K. K. Vasilyev, V. R. Krasheninnikov, A. G. Tashlinskii
Abstract
This paper presents a series of research findings on methods for representation, filtering, parameter estimation (including geometric deformation parameters), detection, and recognition of multidimensional images and their sequences, conducted over 40 years at the scientific school of Ulyanovsk State Technical University, founded by Professor Konstantin Konstantinovich Vasilyev.
{"title":"Research Overview on Statistical Image Analysis Conducted at Ulyanovsk State Technical University","authors":"K. K. Vasilyev, V. R. Krasheninnikov, A. G. Tashlinskii","doi":"10.1134/s1054661823040508","DOIUrl":"https://doi.org/10.1134/s1054661823040508","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper presents a series of research findings on methods for representation, filtering, parameter estimation (including geometric deformation parameters), detection, and recognition of multidimensional images and their sequences, conducted over 40 years at the scientific school of Ulyanovsk State Technical University, founded by Professor Konstantin Konstantinovich Vasilyev.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"6 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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