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 imageanalysis and ev...
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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 imageanalysis 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 imageanalysis 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 imageanalysis and recognition. descriptive image analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in imageanalysis, 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
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 descripti...
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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 imageanalysis. 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 imageanalysis. A necessary condition for such automation is a comparative analysis and optimization of the imageanalysis 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 imageanalysis processes. To do this, it is proposed to define and build an imageanalysis Machine, i.e., TMs specialized for processing spatial information. A method for determining a TM for modeling descriptive algorithmic schemes for imageanalysis is proposed and described. This machine can also be used to evaluate the mathematical characteristics of imageanalysis algorithms. The main concepts and objects of descriptiveanalysis necessary for defining and constructing a TM for modeling and studying procedures for the descriptiveanalysis of images are recalled. An example is given of modeling, on a TM specialized for imageanalysis, information processing procedures implemented in an information structure to generate descriptive algorithmic schemes for imageanalysis when solving imageanalysis problems. The fundamental importance of the results of these studies for the development of the mathematical theory of imageanalysis and their scientific novelty are related to the formulation of problems and the development of methods fo
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 mathematica...
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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 imageanalysis. 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 imageanalysis and recognition process and its constituent procedures is introduced and described - a multilevel model of imageanalysis 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 imageanalysis and recognition processes;(b) the use of multialgorithmic schemes in the imageanalysis 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 imageanalysis: (a) algebraization of imageanalysis;(b) image recognition algorithms accepting spatial information as input data;(c) multiple classifiers (MACs). A new class of models for imageanalysis 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
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 mathematic...
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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 imageanalysis 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 imageanalysis 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
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 imageanalysis. descriptive image analysis is a ...
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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 imageanalysis. 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 imageanalysis 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 descriptiveimage 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 descriptiveimage 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 imageanalysis is understanding of an image as an information object and mathematical object.
The paper is devoted to descriptive image analysis (DA) - a leading line of the modern mathematical theory of imageanalysis. DA is a logically organized set of descriptive methods, mathematical objects, and models an...
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The paper is devoted to descriptive image analysis (DA) - a leading line of the modern mathematical theory of imageanalysis. 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 imageanalysis (the standard IA by Ritter and the descriptive IA by Gurevich).
The paper is devoted to descriptive image analysis (DA) - a leading line of the modern mathematical theory of imageanalysis. DA is a logically organized set of descriptive methods, mathematical objects, and models an...
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The paper is devoted to descriptive image analysis (DA) - a leading line of the modern mathematical theory of imageanalysis. 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 imageanalysis (the standard IA by Ritter and the descriptive IA by Gurevich).
This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of imageanalysis and recognition. The fundamental problem that the subject of ...
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This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of imageanalysis and recognition. The fundamental problem that the subject of the article addresses is automated extraction of information from images necessary for making intelligent decisions. descriptiveanalysis envisages the implementation of imageanalysis 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 imageanalysis 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 imageanalysis 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
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 imageanalysis a...
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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 imageanalysis 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.
The article presents an algebraic model for solving the problem of automation of ophthalmological diagnostics written in the language of descriptiveimage algebras. descriptiveimage algebras are an initial mathematic...
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The article presents an algebraic model for solving the problem of automation of ophthalmological diagnostics written in the language of descriptiveimage algebras. descriptiveimage algebras are an initial mathematical language for formalizing and standardizing representations and procedures for processing image models and conversions over them when extracting information from images. To construct an algebraic model for solving the problem of automation of ophthalmological diagnostics, descriptive algebras of images with one ring are mainly used. This class of algebras belongs to the class of universal linear algebras with a sigma-associative ring with identity. A series of conversions and steps of the algebraic model are described using descriptive Boolean algebras over images. descriptiveimage algebras are the main section of the mathematical apparatus of descriptive image analysis, which is a logically organized set of descriptive methods and models designed for imageanalysis and evaluation. The article defines specialized versions of descriptiveimage algebras with one ring and descriptive Boolean algebras over images, over models and representations of images, and over conversions of image models and images themselves, necessary for constructing an algebraic model. The image models (representations, formalized descriptions) used in writing the article are described. An example of a descriptive algorithmic scheme for solving an applied ophthalmological problem using an algebraic model is constructed.
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