PATTERN RECOGNITION AND IMAGE ANALYSIS最新文献_第7页

Scientific School “Models and Methods for Processing Video Information of Spatially Distributed Data, Pattern Recognition, Geoinformation Technologies” 科学院 "处理空间分布数据视频信息的模型和方法、模式识别、地理信息技术"

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s105466182304051x

D. Yu. Vasin

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Pattern Recognition and Concept Analysis 模式识别和概念分析

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040260

V. K. Leontiev

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Dynamics, Mechanics, Control, and Mathematical Modeling–Scientific and Pedagogical School of Yuri Isaakovich Neimark 动力学、力学、控制和数学建模--尤里-伊萨科维奇-奈马克科学与师范学院

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040399

V. P. Savelyev, D. Yu. Vasin

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Developing the Theory of Stochastic Canonic Expansions and Its Applications 发展随机卡农展开理论及其应用

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040429

I. N. Sinitsyn

{"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}

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Algebra of Orthogonal Series 正交序列代数

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s105466182304034x

A. N. Pankratov, R. K. Tetuev

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Methods and Algorithms for Extracting and Classifying Diagnostic Information from Electroencephalograms and Videos 从脑电图和视频中提取诊断信息并进行分类的方法和算法

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-03-20 DOI: 10.1134/s1054661823040338

Yu. V. Obukhov, I. A. Kershner, D. M. Murashov, R. A. Tolmacheva

{"title":"Methods and Algorithms for Extracting and Classifying Diagnostic Information from Electroencephalograms and Videos","authors":"Yu. V. Obukhov, I. A. Kershner, D. M. Murashov, R. A. Tolmacheva","doi":"10.1134/s1054661823040338","DOIUrl":"https://doi.org/10.1134/s1054661823040338","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article describes new approaches and methods for analyzing long-term EEG data and synchronous video-EEG monitoring of patients with epilepsy and restoration of cognitive functions after moderate traumatic brain injury. EEG analysis is performed using the ridges of its wavelet spectrograms, the power spectral density, the frequency and phase of which, under certain conditions, corresponds to the square of the amplitude, frequency, and phase of the EEG signal. The results of studies of the frequency characteristics of a video stream when analyzing data from long-term synchronous video-EEG monitoring of patients with epilepsy are presented. Signs were obtained for recognizing epileptic seizures and differentiating them from events of a nonepileptic nature. Periodograms of smoothed optical flow calculated from fragments of patient video recordings were analyzed. Welch’s method was used to obtain periodograms. The values of the power spectral density of the optical flow at selected frequencies were used as features. A joint analysis of interchannel frequency synchronization, power spectral density of wavelet spectrogram ridges, and synchronous video made it possible to identify fragments with epileptic seizures on a long-term EEG, excluding various artifacts from consideration. Interchannel phase connectivity of the ridges makes it possible to observe the dynamics of EEG synchronization in patients with moderate traumatic brain injury during cognitive tests. Analysis of a network of phase-related pairs of EEG channels allows determining the positive dynamics of patient rehabilitation.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"371 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884879","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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From Tsetlin’s School of Learning Automata towards Artificial Intelligence 从蔡特林的学习自动机学派走向人工智能

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040478

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Development of Computer Vision, Image Processing, and Analysis at the Digital Optics Laboratory of the Institute for Information Transmission Problems of the Russian Academy of Sciences 俄罗斯科学院信息传输问题研究所数字光学实验室计算机视觉、图像处理和分析的发展情况

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040223

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Image Analysis and Processing Theory, Methods, and Algorithms. Review of Research at the Iconics Laboratory of the Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute) 图像分析与处理理论、方法和算法。俄罗斯科学院信息传输问题研究所（哈尔科维奇研究所）图像学实验室研究综述

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040119

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Methods for Processing and Understanding Image Sequences in Autonomous Navigation Problems 处理和理解自主导航问题中图像序列的方法

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PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2023-12-01 DOI: 10.1134/s1054661823040156

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