Pub Date : 2024-03-20DOI: 10.1134/s1054661823040259
L. I. Kulikova, S. A. Makhortykh
Abstract
A generalized spectral analytical method is outlined—a new approach to processing information arrays. The theoretical foundations of the method are presented, as well as its applications to various problems of processing of experimental data and problems of analysis, recognition, and diagnostics of biomedical and bioinformation systems. Examples of its use for studying biomagnetic data and structures of biomacromolecules are given. The problems of its application for image analysis and recognition are formulated. The method is based on the adaptive decomposition of the original arrays in a functional basis from among classical algebraic systems of polynomials and functions (Jacobi, Chebyshev, Lagrange, Laguerre, and Gegenbauer polynomials, etc., having one and two variables), as well as spherical functions. This approach combines analytical and digital data-processing procedures and is in fact a universal combined technology for processing information arrays.
{"title":"Analysis and Classification of Biomedical and Bioinformation Systems Using a Generalized Spectral Analytical Approach","authors":"L. I. Kulikova, S. A. Makhortykh","doi":"10.1134/s1054661823040259","DOIUrl":"https://doi.org/10.1134/s1054661823040259","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A generalized spectral analytical method is outlined—a new approach to processing information arrays. The theoretical foundations of the method are presented, as well as its applications to various problems of processing of experimental data and problems of analysis, recognition, and diagnostics of biomedical and bioinformation systems. Examples of its use for studying biomagnetic data and structures of biomacromolecules are given. The problems of its application for image analysis and recognition are formulated. The method is based on the adaptive decomposition of the original arrays in a functional basis from among classical algebraic systems of polynomials and functions (Jacobi, Chebyshev, Lagrange, Laguerre, and Gegenbauer polynomials, etc., having one and two variables), as well as spherical functions. This approach combines analytical and digital data-processing procedures and is in fact a universal combined technology for processing information arrays.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884957","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/s1054661823040296
Yu. V. Maslennikov, V. Yu. Slobodchikov, V. A. Krymov, Yu. V. Gulyaev
Abstract
This article presents a review of domestic research on the development of new medical equipment and technologies using equipment and methods of detection of natural magnetic fields of biological objects. The core of the biomagnetic research technology consists of noncontact detection, special mathematical processing, and analysis of the values of the parameters of the magnetic field of the investigated bioobject (generated by the heart, brain, muscles, etc.) found in the specified points of space outside the body of the bioobject using highly sensitive magnetometer equipment, and in particular, using superconducting quantum interference devices (SQUIDs). Based on the studies of myocardial electrophysiology, the samples of technical solutions of magnetometric SQUID-systems for magnetocardiography (MCG), data on diagnostic capabilities, and prospects of practical application of MCG in cardiology are presented. The methodology of magnetocardiographic examination is described and the advantages of MCG application for early diagnosis and control of therapy of various cardiovascular diseases (CVDs) are described. The work of the software of MAG-SCAN diagnostic complexes for the analysis of magnetocardiosignals is illustrated by solving the problem of classifying groups of cardiological patients.
摘要 本文综述了国内利用生物物体天然磁场检测设备和方法开发新型医疗设备和技术的研究情况。生物磁场研究技术的核心是利用高灵敏度磁强计设备，特别是利用超导量子干涉装置（SQUID），对被调查生物物体（由心脏、大脑、肌肉等产生）在生物物体体外指定空间点的磁场参数值进行非接触式检测、特殊数学处理和分析。在心肌电生理学研究的基础上，介绍了用于磁心动图（MCG）的磁测量 SQUID 系统的技术解决方案样本、诊断能力数据以及 MCG 在心脏病学中的实际应用前景。介绍了磁心动图检查的方法，以及应用 MCG 对各种心血管疾病（CVDs）进行早期诊断和控制治疗的优势。通过解决心脏病患者群体分类问题，说明了 MAG-SCAN 诊断综合软件在分析磁心动图信号方面的工作。
{"title":"Magnetometric SQUID Systems and Magnetic Measurement Methods for Biomedical Research","authors":"Yu. V. Maslennikov, V. Yu. Slobodchikov, V. A. Krymov, Yu. V. Gulyaev","doi":"10.1134/s1054661823040296","DOIUrl":"https://doi.org/10.1134/s1054661823040296","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article presents a review of domestic research on the development of new medical equipment and technologies using equipment and methods of detection of natural magnetic fields of biological objects. The core of the biomagnetic research technology consists of noncontact detection, special mathematical processing, and analysis of the values of the parameters of the magnetic field of the investigated bioobject (generated by the heart, brain, muscles, etc.) found in the specified points of space outside the body of the bioobject using highly sensitive magnetometer equipment, and in particular, using superconducting quantum interference devices (SQUIDs). Based on the studies of myocardial electrophysiology, the samples of technical solutions of magnetometric SQUID-systems for magnetocardiography (MCG), data on diagnostic capabilities, and prospects of practical application of MCG in cardiology are presented. The methodology of magnetocardiographic examination is described and the advantages of MCG application for early diagnosis and control of therapy of various cardiovascular diseases (CVDs) are described. The work of the software of MAG-SCAN diagnostic complexes for the analysis of magnetocardiosignals is illustrated by solving the problem of classifying groups of cardiological patients.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"284 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885025","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/s1054661823040235
A. S. Krylov, A. V. Nasonov, D. V. Sorokin, A. V. Khvostikov, E. A. Pavelyeva, Ya. A. Pchelintsev
Abstract
General methods of image processing, analysis and enhancement and their biomedical applications developed by the scientific school of the Laboratory of Mathematical Methods of Image Processing of the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University are reviewed. The suggested general methods and algorithms of image quality enhancement for image resampling and super-resolution, ringing artifact reduction, image sharpening, image denoising, and image registration are described. Image analysis methods based on Hermite projection method, Gauss-Laguerre functions and the use of phase information are presented. We describe and review the developed methods for medical imaging tasks solution, including problems in histology, color Doppler flow mapping, ultrasound liver fibrosis diagnostics, CT brain perfusion, Alzheimer’s disease diagnostics, dermatology, chest X-ray image analysis, live cell image registration, tracking, segmentation and synthesis. The paper illustrates the basic research idea of the effectiveness of the hybrid approach when we jointly use classical mathematical methods and deep learning approaches.
摘要 综述了莫斯科国立罗蒙诺索夫大学计算数学与控制论学院图像处理数学方法实验室科学流派开发的图像处理、分析和增强的一般方法及其生物医学应用。文中介绍了针对图像重采样和超分辨率、减少振铃伪影、图像锐化、图像去噪和图像配准的图像质量提升建议的一般方法和算法。介绍了基于 Hermite 投影法、高斯-拉盖尔函数和使用相位信息的图像分析方法。我们描述并回顾了已开发的医学成像任务解决方案，包括组织学、彩色多普勒血流图、超声肝纤维化诊断、CT 脑灌注、老年痴呆症诊断、皮肤病学、胸部 X 射线图像分析、活细胞图像配准、跟踪、分割和合成等方面的问题。本文阐述了我们联合使用经典数学方法和深度学习方法时混合方法有效性的基本研究思路。
{"title":"Image Analysis and Enhancement: General Methods and Biomedical Applications","authors":"A. S. Krylov, A. V. Nasonov, D. V. Sorokin, A. V. Khvostikov, E. A. Pavelyeva, Ya. A. Pchelintsev","doi":"10.1134/s1054661823040235","DOIUrl":"https://doi.org/10.1134/s1054661823040235","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>General methods of image processing, analysis and enhancement and their biomedical applications developed by the scientific school of the Laboratory of Mathematical Methods of Image Processing of the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University are reviewed. The suggested general methods and algorithms of image quality enhancement for image resampling and super-resolution, ringing artifact reduction, image sharpening, image denoising, and image registration are described. Image analysis methods based on Hermite projection method, Gauss-Laguerre functions and the use of phase information are presented. We describe and review the developed methods for medical imaging tasks solution, including problems in histology, color Doppler flow mapping, ultrasound liver fibrosis diagnostics, CT brain perfusion, Alzheimer’s disease diagnostics, dermatology, chest X-ray image analysis, live cell image registration, tracking, segmentation and synthesis. The paper illustrates the basic research idea of the effectiveness of the hybrid approach when we jointly use classical mathematical methods and deep learning approaches.</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":"140885119","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/s1054661823040272
S. A. Makhortykh, A. V. Moskalenko
Abstract
Theoretical approaches to solving problems of diagnosing and predicting cardiac dysfunctions within the framework of modern mathematical physics of the heart (cardiophysics) are presented. The possibilities for diagnostic purposes of using recognition of patterns of behavior of autowave vortices that arise in the heart during dangerous disturbances in its functioning are considered. The prospects for the development of new methods for diagnosing patterns of the basic heart rhythm in the development of already recognized methods for analyzing heart rate variability are discussed.
{"title":"The Possibilities of Diagnosis and Prediction of Cardiac Disorders Based on the Results of Mathematical Modeling of the Myocardium and Regulation of Action of the Heart","authors":"S. A. Makhortykh, A. V. Moskalenko","doi":"10.1134/s1054661823040272","DOIUrl":"https://doi.org/10.1134/s1054661823040272","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Theoretical approaches to solving problems of diagnosing and predicting cardiac dysfunctions within the framework of modern mathematical physics of the heart (cardiophysics) are presented. The possibilities for diagnostic purposes of using recognition of patterns of behavior of autowave vortices that arise in the heart during dangerous disturbances in its functioning are considered. The prospects for the development of new methods for diagnosing patterns of the basic heart rhythm in the development of already recognized methods for analyzing heart rate variability are discussed.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"62 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885123","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/s105466182304034x
A. N. Pankratov, R. K. Tetuev
Abstract
An algebra of orthogonal series has been developed for the operations of multiplication and division, differentiation and integration of signals represented by series of classical orthogonal polynomials and functions. It is shown that the considered linear transformations for classical orthogonal polynomials and functions are subject to recurrence relations of a special form, which makes it possible to perform these transformations over series in the space of expansion coefficients. A theorem on the condition for the existence of a recurrence relation for the inverse transformation is proven. A general scheme of an algorithm for calculating the coefficients of the resulting series from the coefficients of the original series through the coefficients of recurrent relations is proposed. It has been proven that the linear transformation of a series of length (N) is executed by a linear complexity algorithm ({{Theta }}left( N right)). Formulas for the Chebyshev, Jacobi, Laguerre, and Hermite polynomials are given.
摘要 针对经典正交多项式和函数序列所代表信号的乘除、微分和积分运算，开发了一种正交序列代数。研究表明，所考虑的经典正交多项式和函数的线性变换受制于特殊形式的递推关系，这使得在膨胀系数空间中对序列进行这些变换成为可能。证明了关于逆变换递推关系存在条件的定理。提出了通过递推关系系数从原数列系数计算所得数列系数的算法的一般方案。证明了长度为 (N) 的数列的线性变换是通过线性复杂性算法 ({{Theta }}left( N right))执行的。给出了切比雪夫、雅可比、拉盖尔和赫米特多项式的公式。
{"title":"Algebra of Orthogonal Series","authors":"A. N. Pankratov, R. K. Tetuev","doi":"10.1134/s105466182304034x","DOIUrl":"https://doi.org/10.1134/s105466182304034x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>An algebra of orthogonal series has been developed for the operations of multiplication and division, differentiation and integration of signals represented by series of classical orthogonal polynomials and functions. It is shown that the considered linear transformations for classical orthogonal polynomials and functions are subject to recurrence relations of a special form, which makes it possible to perform these transformations over series in the space of expansion coefficients. A theorem on the condition for the existence of a recurrence relation for the inverse transformation is proven. A general scheme of an algorithm for calculating the coefficients of the resulting series from the coefficients of the original series through the coefficients of recurrent relations is proposed. It has been proven that the linear transformation of a series of length <span>(N)</span> is executed by a linear complexity algorithm <span>({{Theta }}left( N right))</span>. Formulas for the Chebyshev, Jacobi, Laguerre, and Hermite polynomials are given.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"147 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885120","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/s1054661823040338
Yu. V. Obukhov, I. A. Kershner, D. M. Murashov, R. A. Tolmacheva
Abstract
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.
{"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}
Pub Date : 2023-12-01DOI: 10.1134/s1054661823040478
Abstract
Though there is a widely spread opinion that Artificial Intelligence had been formulated as an independent science first of all in the United States, where it was supported with computer science, the present paper demonstrates that in Russia, AI development went through the study of some fundamental questions underlying intelligent activity applicable to various scientific and technical fields such as biology, engineering, linguistics, probability, control theory, and many others. The high scientific level of the school of Professor Mikhail Tsetlin is illustrated below via three important problems that confirm the role and the quality of his school at the Lomonosov Moscow State University, aimed towards development creative abilities among students, permitting them to formulate new and extremely promising tasks as a result of their intensive discussion in the main seminars of this school.
{"title":"From Tsetlin’s School of Learning Automata towards Artificial Intelligence","authors":"","doi":"10.1134/s1054661823040478","DOIUrl":"https://doi.org/10.1134/s1054661823040478","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Though there is a widely spread opinion that Artificial Intelligence had been formulated as an independent science first of all in the United States, where it was supported with computer science, the present paper demonstrates that in Russia, AI development went through the study of some fundamental questions underlying intelligent activity applicable to various scientific and technical fields such as biology, engineering, linguistics, probability, control theory, and many others. The high scientific level of the school of Professor Mikhail Tsetlin is illustrated below via three important problems that confirm the role and the quality of his school at the Lomonosov Moscow State University, aimed towards development creative abilities among students, permitting them to formulate new and extremely promising tasks as a result of their intensive discussion in the main seminars of this school.</p> </span>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"154 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198895","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 : 2023-12-01DOI: 10.1134/s1054661823040223
Abstract
Optical and digital images are one of the most important channels for transmitting information. At the Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS), this topic has always, since the founding of the institute, been given the closest attention. The institute’s employees have made significant contributions to both the domestic and global science of image processing. In this work, the authors touched only on the main stages of the development of the theory, methods, and algorithms for image processing and analysis in the Laboratory of Digital Optics of the Institute for Information Transmission Problems of the Russian Academy of Sciences.
{"title":"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","authors":"","doi":"10.1134/s1054661823040223","DOIUrl":"https://doi.org/10.1134/s1054661823040223","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Optical and digital images are one of the most important channels for transmitting information. At the Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS), this topic has always, since the founding of the institute, been given the closest attention. The institute’s employees have made significant contributions to both the domestic and global science of image processing. In this work, the authors touched only on the main stages of the development of the theory, methods, and algorithms for image processing and analysis in the Laboratory of Digital Optics of the Institute for Information Transmission Problems of the Russian Academy of Sciences.</p> </span>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"142 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198873","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 : 2023-12-01DOI: 10.1134/s1054661823040119
Abstract
A historical and analytical review is given of the development of the Iconics Laboratory at the Institute for Information Transmission Problems of the Russian Academy of Sciences since the establishment of the Institute. The main research areas of the laboratory are discussed including image models, spatial and frequency methods of video data analysis and processing, issues of distortion elimination, image decomposition and enhancement, object detection, image segmentation; application of the developed methods for processing space images of planets; issues of constructing specialized image processing systems, and others.
{"title":"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)","authors":"","doi":"10.1134/s1054661823040119","DOIUrl":"https://doi.org/10.1134/s1054661823040119","url":null,"abstract":"<span> <h3>Abstract</h3> <p>A historical and analytical review is given of the development of the Iconics Laboratory at the Institute for Information Transmission Problems of the Russian Academy of Sciences since the establishment of the Institute. The main research areas of the laboratory are discussed including image models, spatial and frequency methods of video data analysis and processing, issues of distortion elimination, image decomposition and enhancement, object detection, image segmentation; application of the developed methods for processing space images of planets; issues of constructing specialized image processing systems, and others.</p> </span>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"142 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198896","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 : 2023-12-01DOI: 10.1134/s1054661823040156
Abstract
This article presents the results of research into methods and algorithms for processing image sequences aimed at application in autonomous navigation systems. The methods are grouped in the following areas: pre-processing, generation of 2D and 3D scene models, object recognition. and determination of motion parameters from a sequence of images. The article presents methods and algorithms proposed and researched by the authors of the article over the past decade. Description of the methods and algorithms is accompanied by examples and results of experimental studies.
{"title":"Methods for Processing and Understanding Image Sequences in Autonomous Navigation Problems","authors":"","doi":"10.1134/s1054661823040156","DOIUrl":"https://doi.org/10.1134/s1054661823040156","url":null,"abstract":"<span> <h3>Abstract</h3> <p>This article presents the results of research into methods and algorithms for processing image sequences aimed at application in autonomous navigation systems. The methods are grouped in the following areas: pre-processing, generation of 2D and 3D scene models, object recognition. and determination of motion parameters from a sequence of images. The article presents methods and algorithms proposed and researched by the authors of the article over the past decade. Description of the methods and algorithms is accompanied by examples and results of experimental studies.</p> </span>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":"86 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198897","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号