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On Some Scientific Results of the ICPR-2020

pattern RECOGNITION AND image analysis 2021年第3期31卷 357-363页

作者： Gurevich, I. B. Moroni, D. Pascali, M. A. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia Pisa Natl Res Council Natl Res Council Italy Res Area Pisa Inst Informat Sci & Technol A Faedo I-56124 Pisa Italy

This special issue of PRIA is devoted to some scientific results and trends of the 25th International Conference on pattern Recognition (Virtual, Milano, Italy, January 10-15, 2021). Two important events of ICPR-2020 are represented in this special issue: (1) The paper of Professor Ching Yee Suen (Centre for pattern Recognition and Machine Intelligence, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada)-the recent winner of IAPR very prestigious K.S. Fu Prize for a year of 2020. The paper based on his lecture "From handwriting to human personality and facial beauty" presented at the ICPR 2020;(2) Special issue "ICPR-2020 Workshop "image Mining. Theory and Applications." The analysis of the scientific contribution of IMTA-VII-2021 allows us to draw the following conclusions: (1) The construction of a unified mathematical theory of image analysis is still far from complete. (2) There is considerable interest in the development of new mathematical methods for analyzing and evaluating information presented in the form of images. (3) There is a tendency to expand the mathematical apparatus in the development of new methods of image analysis and recognition by involving in this process areas of mathematics that were not previously used in image analysis. (4) The gap between the capabilities of new mathematical methods of image analysis and recognition and their actual use in solving applied problems remains significant. (5) There is an excessive use of neural networks in solving applied problems of image analysis and image recognition, and quite often without proper justification and interpretation of the results. The special issue includes articles based on the workshop papers selected by the IMTA-VII-2021 Program Committee for publication in PRIA. The PRIA special issue "Scientific Resume of the 25th International Conference on pattern Recognition" is prepared by the National Committee for pattern Recognition and image A

关键词： automation of data analysis biomedical applications biomedical images conditional generative adversarial networks data mining descriptive image analysis face beauty identity-preserved image mining intellectual data analysis mathematical theory of image analysis mathematical theory of pattern recognition

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SHAPE RECONSTRUCTION ON A VARYING MESH

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IEEE TRANSACTIONS ON pattern analysis AND MACHINE INTELLIGENCE 1990年第4期12卷 345-362页

作者： WEISS, I Center for Autom. Res. Maryland Univ. College Park MD USA

A central class of image understanding problems is concerned with reconstructing a shape from an incomplete data set, such as fitting a surface to (partially) given contours. A new theory for solving such problems is presented. Unlike the current heuristic methods, the method used starts from fundamental principles that should be followed by any reconstruction method, regardless of its mathematical or physical implementation. A mathematical procedure which conforms to these principles is presented. One major advantage of the method is the ability to handle shapes containing both smooth and sharp parts without using thresholds. A sharp variation, such as a corner, requires a high-resolution mesh for adequate representation, while slowly varying sections can be represented with sparser mesh points. Unlike current methods, this procedure fits the surface on a varying mesh. The mesh is constructed automatically to be more dense at parts of the image that have more rapid variation. Analytical examples are given in simple cases, followed by numerical experiments.

关键词： Shape image reconstruction Surface fitting Noise shaping Surface reconstruction Smoothing methods Reconstruction algorithms Curve fitting image converters image processing INSPEC: Controlled Indexing picture processing pattern recognition INSPEC: Non-Controlled Indexing varying mesh picture processing pattern recognition shape reconstruction image understanding high-resolution mesh

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Descriptive approach to image analysis: image models

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pattern Recognition and image analysis 2008年第4期18卷 518-541页

作者： Gurevich, I.B. Yashina, V.V. Dorodnicyn Computing Center Russian Academy of Sciences ul. Vavilova 40 Moscow 119333 Russia

The brief review of main methods and features of the descriptive approach to image analysis (DAIA), viz. forming the system of concepts that characterize the initial information-images-in recognition problems, and descriptive image models designed for recognition problems, is given. At present, in terms of development of image analysis and recognition, it is critical to understand the nature of the initial information, viz. images, find methods of image representation and description to be used to construct image models designed for recognition problems, establish the mathematical language for the unified description of image models and their transformations that allow constructing image models and solving recognition problems, construct models to solve recognition problems in the form of standard algorithmic schemes that allow, in the general case, moving from the initial image to its model and from the model to the sought solution. The DAIA gives a single conceptual structure that helps develop and implement these models and the mathematical language. The main DAIA purpose is to structure and standardize different methods, operations and representations used in image recognition and analysis. The DAIA provides the conceptual and mathematical basis for image mining, with its axiomatic and formal configurations giving the ways and tools to represent and describe images to be analyzed and evaluated. © 2008 Pleiades Publishing, Ltd.

关键词： image analysis

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image formalization space: Formulation of tasks, structural properties, and elements

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pattern Recognition and image analysis 2011年第2期21卷 134-139页

作者： Gurevich, I.B. Yashina, V.V. Dorodnicyn Computing Center Russian Academy of Sciences ul. Vavilova 40 Moscow 119333 Russia

The work is devoted to developing the main results in solving the fundamental problem of formalization and systematization of methods and forms of information representation in image analysis, recognition, and understanding tasks. This problem arises in connection with development of the descriptive approach to image analysis and understanding. The main result is the concept of image spatial formalization, including a set of image states and a set of states of image transformation schemes. We consider the construction of algorithmic schemes generating phase trajectories for solving problems of image analysis and recognition. We present axioms defining the properties and structure of the image formalization space. The introduced system of concepts constitute the basis for standardizing methods for synthesizing image models oriented to image analysis and understanding tasks. © 2011 Pleiades Publishing, Ltd.

关键词： image analysis

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image analysis and Enhancement: General methods and Biomedical Applications

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pattern RECOGNITION AND image analysis 2023年第4期33卷 1493-1514页

作者： Krylov, A. S. Nasonov, A. V. Sorokin, D. V. Khvostikov, A. V. Pavelyeva, E. A. Pchelintsev, Ya. A. Lomonosov Moscow State Univ Fac Computat Math & Cybernet Lab Math Methods Image Proc Moscow 119991 Russia

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.

关键词： image analysis image processing image enhancement medical images cell microscopy images mathematical methods convolutional neural networks hybrid methods

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Descriptive image analysis: Part II. Descriptive image Models

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pattern RECOGNITION AND image analysis 2019年第4期29卷 598-612页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

The article is the second in a series on the current state and prospects of Descriptive image analysis, which is the leading branch of the modern mathematical theory of image analysis. Descriptive image analysis is a logically organized set of descriptive methods and models for analyzing and evaluating information in the form of images and for automating knowledge and data extraction from images necessary for making intelligent decisions about real-world scenes displayed and represented in an analyzed image. Problems on making intelligent decisions based on data analysis require formal representation of the source information, ideally, a mathematical model. image modeling has a long, but not very productive history. Therefore, in the Descriptive Approach to image analysis and understanding (DA), the primary problem is bringing an image to a form suitable for recognition. The DA interprets the sought representation in the form of a descriptive image model (DIM). Due to the extremely complex informational nature and technical features involved in the digital representation of an image, it is impossible to construct a classical mathematical model of an image as an information object. To overcome this complexity and regularize the problem of bringing an image to a form convenient for recognition, a new mathematical object, a DIM is introduced and used in the DA. Models of recognition objects-images-and definitions of transformations over image models are considered. A formalized concept of descriptive image models is proposed. The results can be used to create a basis for methods of transforming and understanding an image as a mathematical object. The article's main contribution to developing the mathematical theory of image analysis is understanding of an image as an information object and mathematical object.

关键词： image analysis descriptive image analysis descriptive approach mathematical theory of image analysis descriptive image models image representation

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analysis of mathematical morphology for quantifying galaxy distributions in N-body simulations

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PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF JAPAN 1999年第4期51卷 435-444页

作者： Ueda, H Akita Univ Fac Educ & Human Studies Akita 0108502 Japan

We used a counts-in-cells analysis for quantifying galaxy distributions in cosmological N-body simulations. We adopted seven simulations which were evolved from different power-law initial conditions and CDM initial conditions with different density parameter. In our analyses, we focused on information of the galaxy distributions in two-dimensions, and adopted a mathematical morphology which is one of the most noticeable fields in image analysis. We propose new statistical measures which are based on the mathematical morphology. These measures relate to the area of the counts-in-cells or the pattern spectrum. From our analyses, the histogram of the area, the pattern spectrum, and these new measures are found to be useful to distinguish the difference of the galaxy distributions among power-law models as well as CDM models.

关键词： cosmology galaxies : general gravitation numerical methods

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Computer science: Subject, fundamental research problems, methodology, structure, and applied problems

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pattern Recognition and image analysis 2014年第3期24卷 333-346页

作者： Gurevich, I.B. Zhuravlev, Yu. I. Dorodnicyn Computing Centre Russian Academy of Sciences ul. Vavilova 40 Moscow119333 Russia

The work is devoted to computer science. The subject, fundamental research problems, methodology, structure, and applied problems are defined and analyzed. The mathematical apparatus of computer science and its main methods—formalization, algorithmization, mathematical modeling, and programming—are considered. A characterization is given to the main fields of computer science pattern recognition, image analysis, artificial intelligence, intelligent data analysis, and information technologies. An in-depth analysis is carried out of the relationship and interaction between computer science and cybernetics. The role and the subject of informatics are discussed. © 2014, Pleiades Publishing, Ltd.

关键词： image analysis

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Descriptive image analysis: Genesis and current trends

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pattern Recognition and image analysis 2017年第4期27卷 653-674页

作者： Gurevich, I.B. Yashina, V.V. Federal Research Center Computer Science and Control Russian Academy of Sciences ul. Vavilova 44/2 Moscow119333 Russia

This paper is devolved to descriptive image analysis, an important, if not a leading, direction in the modern mathematical theory of image analysis. Descriptive image analysis is a logically organized set of descriptive methods and models meant for analyzing and estimating the information represented in the form of images, as well as for automating the extraction (from images) of knowledge and data needed for intelligent decision making about the real-world scenes reflected and represented by images under analysis. The basic idea of descriptive image analysis consists in reducing all processes of analysis (processing, recognition, and understanding) of images to (1) construction of models (representations and formalized descriptions) of images;(2) definition of transformations over image models;(3) construction of models (representations and formalized descriptions) of transformations over models and representations of images;and (4) construction of models (representations and formalized descriptions) of schemes of transformations over models and representations of images that provide the solution to image analysis problems. The main fundamental sources that predetermined the origination and development of descriptive image analysis, or had a significant influence thereon, are considered. In addition, a brief description of the current state of descriptive image analysis that reflects the main results of the descriptive approach to analysis and understanding of images is presented. The opportunities and limitations of algebraic approaches to image analysis are discussed. During recent years, it was accepted that algebraic techniques, particularly, different kinds of image algebras, are the most promising direction of construction of the mathematical theory of image analysis and of the development of a universal algebraic language for representing image analysis transforms, as well as image representations and models. The main goal of the algebraic approaches is desi

关键词： pattern recognition

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On possibilities of using pattern recognition methods to study mathematical models

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pattern Recognition and image analysis 2012年第1期22卷 144-149页

作者： Neimark, Yu I. Teklina, L.G. Research Institute for Applied Mathematics and Cybernetics of Nizhni Novgorod State University ul. Ul'yanova 10 Nizhni Novgorod 603005 Russia

The possibilities of using pattern recognition methods to study mathematical models with a large number of parameters are discussed. The principal point is to study models by constructing phase and parametric portraits. This allows one to solve problems of predicting the states of the object described by the mathematical model in hand and controlling the object and analyzing and studying problems that follow from the particular content of the model. Examples of three mathematical models are given to illustrate this problem. © 2012 Pleiades Publishing, Ltd.

关键词： pattern recognition

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