Descriptive Image Analysis

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
I. B. Gurevich, V. V. Yashina
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引用次数: 0

Abstract

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 to the equivalence class of admissible transformations. As part of Descriptive Image Analysis, the following main results were obtained: (1) new mathematical objects were introduced and studied: image formalization space, descriptive image algebras, descriptive algorithmic schemes; (2) descriptive image analysis models have been defined and studied: image models, image transformation models, models for generating descriptive algorithmic schemes; (3) linguistic and knowledge-oriented tools have been developed to support the automation of image analysis; (4) a number of automated software systems have been developed and axioms for Descriptive Image Analysis proposed. A general description of the provisions of Descriptive Image Analysis is presented, and the main results of research in the first two directions are discussed: new mathematical objects and image analysis models. A comprehensive bibliography on Descriptive Image Analysis is provided.

Abstract Image

描述性图像分析
摘要 概述了描述性图像分析的主要方法、模型和结果。描述性图像分析法是一套逻辑严密的描述性方法和模型,用于图像分析和评估。描述性图像分析法的方法、模型和结果决定了描述性图像分析法的发展现状和趋势。随着描述性方法分析和理解图像的方法和设备的发展和完善,人们提出了对其的解释,并将其定义为描述性图像分析。描述性图像分析法的主要目标是将图像分析和识别中使用的各种方法、过程和概念结构化和标准化。描述性图像分析解决了图像分析、识别和理解问题中信息表示方法和形式的形式化和系统化的基本问题,特别是与自动从图像中提取信息以做出智能决策(诊断、预测、检测、评估和识别物体、事件和过程的模式)相关的问题。描述性图像分析既可以解决与构建图像作为识别对象的形式描述有关的问题,也可以解决识别和理解图像的综合程序问题。有人认为,对以图像形式表示的信息(问题解决方案轨迹)进行分析和评估的过程,一般可视为一系列中间和最终(确定解决方案)估计值的变换和计算的序列/组合。这些变换由图像的等价类及其表示法定义。后者的定义是描述性的,即使用一组基本原型和相应的生成变换,这些变换在功能上与可允许变换的等价类是完整的。作为描述性图像分析的一部分,取得了以下主要成果:(1) 引入并研究了新的数学对象:图像形式化空间、描述性图像代数、描述性算法方案;(2) 定义并研究了描述性图像分析模型:图像模型、图像变换模型、生成描述性算法方案的模型;(3) 开发了支持图像分析自动化的语言和知识导向工具;(4) 开发了一些自动化软件系统,并提出了描述性图像分析公理。本文对描述性图像分析的规定进行了总体描述,并讨论了前两个方向的主要研究成果:新的数学对象和图像分析模型。还提供了有关描述性图像分析的综合参考书目。
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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍: The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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