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PATTERN RECOGNITION AND image analysis 2023年第4期33卷 784-839页

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

An overview of the main methods, models, and results of descriptive image analysis is given. descriptive image analysis is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The state of the art and trends in the development of descriptive image analysis are determined by the methods, models, and results of the descriptive Approach to image analysis and understanding. As the methods and apparatus of the descriptive Approach to the analysis and understanding of images were developed and refined, its interpretation was proposed, defined as descriptive image analysis. The main goal of descriptive image analysis is to structure and standardize the various methods, processes, and concepts used in image analysis and recognition. descriptive image analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in image analysis, recognition, and understanding problems, in particular, associated with automating the extraction of information from images to make intelligent decisions (diagnosis, prediction, detection, assessment, and identification patterns of objects, events and processes). descriptive image analysis makes it possible to solve both problems related to constructing formal descriptions of images as recognition objects and problems of synthesizing procedures for recognizing and understanding images. It is suggested that the processes of analysis and evaluation of information represented in the form of images (problem solution trajectories) can generally be considered a sequence/combination of transformations and calculations of a set of intermediate and final (determining the solution) estimates. These transformations are defined by equivalence classes of images and their representations. The latter are defined descriptively, i.e., using a basic set of prototypes and corresponding generating transformations that are functionally complete with respect t

关键词： descriptive approach to image analysis and understanding mathematical theory of image analysis descriptive image analysis image formalization space descriptive image algebras descriptive image algebras with one ring descriptive image models efficient algorithms for recognizing spatial information descriptive algorithmic schemes descriptive algorithmic representation schemes images multiple classifiers combination of algorithms combination of data dual image representations reducing images to a recognizable form generating descriptive trees image representations axioms for descriptive image analysis

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descriptive image analysis: Part III. Multilevel Model for Algorithms and Initial Data Combining in Pattern Recognition

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PATTERN RECOGNITION AND image analysis 2020年第3期30卷 328-341页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This is the third article in a series of publications devoted to the current state and future prospects of descriptive image analysis (DIA), one of the leading and intensively developing branches of modern mathematical theory of image analysis. The fundamental problem of computer science touched by the article is the automation of extracting from images of information necessary for intellectual decision-making. A new class of models for the image analysis and recognition process and its constituent procedures is introduced and described - a multilevel model of image analysis and recognition procedures (MMCAI) - which is based on the joint use of methods of combining algorithms and methods of combining fragmentary initial data - partial descriptions of the object of analysis and recognition - an image. The architecture, functionality, limitations, and characteristics of the MMCAI are justified and defined. The main properties of the MMCAI class are as follows: (a) combining the fragments of the initial data and their representations and combining algorithms at all levels of image analysis and recognition processes;(b) the use of multialgorithmic schemes in the image analysis and recognition process;and (c) the use of dual representations of images as input data for the analysis and recognition algorithms. The problems arising in the development of the MMCAI are closely related to the development of the following areas of the modern mathematical theory of image analysis: (a) algebraization of image analysis;(b) image recognition algorithms accepting spatial information as input data;(c) multiple classifiers (MACs). A new class of models for image analysis is introduced in order to provide the following possibilities: (a) standardization, modeling, and optimization of descriptive Algorithmic Schemes (DAS) that form the brainware of the MMCAI and processing heterogeneous ill-structured information - dual representations - spatial, symbolic, and numerical representations

关键词： image analysis descriptive image analysis mathematical theory of image analysis models of image analysis procedures image representations image models descriptive image representations descriptive image models dual representation of images combinatorial structures of local neighborhoods multiple classifiers support sets image recognition algorithms

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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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descriptive image analysis: Part IV. Information Structure for Generating descriptive Algorithmic Schemes for image Recognition

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PATTERN RECOGNITION AND image analysis 2020年第4期30卷 638-654页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is the fourth in a series of publications devoted to the state of the art and prospects for developing descriptive image analysis, one of the leading and intensively developing fields of modern mathematical image analysis theory. The fundamental problem discussed in the article is to automate information extraction from images necessary for making intelligent decisions. This study is devoted to regularizing the generation of descriptive algorithmic image analysis and recognition schemes. The main result is the definition of a new mathematical structure with the following functional capabilities: 1) solution of an image recognition problem in a given formulation, with given initial data and a scenario that determines the sequence of application of information processing procedures and their iterative loops;2) construction of descriptive algorithmic schemes for solving a problem with given initial data in the absence of a given scenario;in the case of a successful solution, the fixation of the sequence of procedures and information processing loops that yielded its solution governs the corresponding descriptive algorithmic schemes and scenarios that can be further used to solve the corresponding class of image recognition problems;3) comparative analysis and optimization of methods for solving image recognition problems via their realization as descriptive algorithmic schemes and scenarios allowed by the structure. The introduced structure is a tool for representing and implementing information processing while solving an image recognition problem for arbitrary formulations, scenarios, models, and solution methods;it can also emulate any descriptive algorithmic scheme and combinations thereof, which are used and generated when solving an image recognition problem. The introduced structure is interpreted as a fundamental model for generating and emulating image recognition procedures. A type characteristic of the introduced information structure for genera

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated information extraction from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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descriptive image analysis. Foundations and descriptive image Algebras

Descriptive Image Analysis. Foundations and Descriptive Imag...

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International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) / 30th Anniversary Conference of the Centre-for-Pattern-Recognition-and-Machine-Intelligence (CENPARMI)

作者： Gurevich, Igor Yashina, Vera Russian Acad Sci Fed Res Ctr Comp Sci & Control 44 Vavilov StrBldg 2 Moscow 119333 Russia

The paper is devoted to descriptive image analysis (DA) - a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating 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. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images;(2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).

关键词： image analysis descriptive image analysis descriptive approach to image analysis image mining mathematical theory of image analysis descriptive image algebras image algebras descriptive models image representations pattern recognition

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On Modeling descriptive image analysis Procedures on a Specialized Turing Machine

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PATTERN RECOGNITION AND image analysis 2022年第3期32卷 469-476页

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

This article is devoted to an original approach to the definition and description of a Turing machine (TM) for implementing descriptive image analysis methods based on an information structure for generating descriptive algorithmic schemes for automating image analysis. The fundamental problem, to which the subject of the study belongs, is the automation of extracting information from images that is necessary for making intelligent decisions. One of the important and promising areas of research in this problem is the automation of the choice of a method for solving the problem of image analysis. A necessary condition for such automation is a comparative analysis and optimization of the image analysis algorithms, which, in turn, requires estimates of the complexity and efficiency of algorithms and a universal calculator to obtain them. One of the strategic goals for the development of descriptive image analysis is the study of models of image analysis processes. To do this, it is proposed to define and build an image analysis Machine, i.e., TMs specialized for processing spatial information. A method for determining a TM for modeling descriptive algorithmic schemes for image analysis is proposed and described. This machine can also be used to evaluate the mathematical characteristics of image analysis algorithms. The main concepts and objects of descriptive analysis necessary for defining and constructing a TM for modeling and studying procedures for the descriptive analysis of images are recalled. An example is given of modeling, on a TM specialized for image analysis, information processing procedures implemented in an information structure to generate descriptive algorithmic schemes for image analysis when solving image analysis problems. The fundamental importance of the results of these studies for the development of the mathematical theory of image analysis and their scientific novelty are related to the formulation of problems and the development of methods fo

关键词： image analysis descriptive image analysis Turing machine for descriptive image analysis image analysis machine mathematical theory of image analysis image-mining descriptive algorithmic schemes mathematical structures information structures computational procedures formalization computational procedures formalization standart representations

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descriptive image analysis. Foundations and descriptive image Algebras

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第11期33卷

作者： Gurevich, Igor Yashina, Vera Russian Acad Sci Fed Res Ctr Comp Sci & Control 44 Vavilov StrBldg 2 Moscow 119333 Russia

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descriptive Models of Information Transformation Processes in image analysis

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PATTERN RECOGNITION AND image analysis 2021年第3期31卷 402-420页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of image analysis and recognition. The fundamental problem that the subject of the article addresses is automated extraction of information from images necessary for making intelligent decisions. descriptive analysis envisages the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation, which is converted from the original form to one convenient for recognition (i.e., to a model), and data representation conversion models. The image analysis processes are considered to be sequences of transformations implemented in the phase space and providing the construction of phase states of the image, which together form the phase trajectory of image transfer from the original form to the model. The study of the image formalization space leads to formalization of the concepts of representation/image model, as well as to construction of models of the image recognition and analysis processes and formulation of mathematical statements of image recognition and analysis problems. Two types of image analysis models are considered: (1) models reflecting the methodology and mathematical foundations of image recognition and analysis the problem statement, used mathematical and heuristic methods, algorithmic content of the process: (a) a model based on reverse algebraic closure, (b) a model based on the property of equivalence of images of the same class, (c) a model based on multiple image models and multiple classifiers;(2) models characterizing the architecture and structure of the recognition process: (a) a multilevel model for combining algorithms and initial data for image recognition, (b) information structure for generating descriptive algorithmic image recognition schemes. A brief description of the above models is given. A comparative an

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated extraction of information from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Multialgorithmic Hierarchical image analysis System: Architecture and analysis Model

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PATTERN RECOGNITION AND image analysis 2024年第4期34卷 959-965页

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

The article gives a detailed description of one of the basic models of descriptive image analysis, characterizing the architecture and structure of the image recognition process: a multilevel model of image analysis and recognition procedures with joint use of methods for combining algorithms and fragmentary initial data-partial descriptions of the object of analysis and image recognition. The architecture, functional capabilities, limitations, and characteristics of a multilevel model for combining algorithms and initial data in image recognition are substantiated and defined.

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On Some Scientific Results of the ICPR-2020

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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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