Abstract

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 of the initial data; (b) comparative analysis, standardization, modeling, and optimization of different algorithms for the analysis and recognition of spatial information. The fundamental importance of the results of these studies for the development of the mathematical theory of image analysis and their scientific novelty are associated with the statement of problems and the development of methods for modeling the processes of automation of image analysis when ill structured representations of images, including spatial data proper – images and their fragments, image models, incompletely formalized representations, and subsets of combinations of these representations – are used as the initial data. The introduction of the MMCAI as a standard structure for representing algorithms for the analysis and recognition of two-dimensional information and dual representations of images allow one to generalize and substantiate well-known heuristic recognition algorithms and assess their mathematical properties and applied utility.

中文翻译：

---

描述性图像分析：第三部分。模式识别中算法与初始数据组合的多级模型

摘要

这是系列出版物中的第三篇，专门介绍描述性图像分析（DIA）的现状和未来，DIA是图像分析现代数学理论的领先和深入发展的分支之一。本文所涉及的计算机科学的基本问题是从图像中自动提取智力决策所需的信息。引入并描述了用于图像分析和识别过程的新模型及其组成过程模型-图像分析和识别过程的多级模型（MMCAI），该模型基于联合使用的组合算法和组合方法零碎的初始数据–分析和识别对象的部分描述–图像。架构，功能，局限性，并定义了MMCAI的特征。MMCAI类的主要属性如下：（a）组合初始数据的片段及其表示，并在图像分析和识别过程的各个级别上组合算法；（b）在图像分析和识别过程中使用多重算法方案；（c）使用图像的双重表示作为分析和识别算法的输入数据。MMCAI的发展中出现的问题与现代图像分析数学理论的以下领域的发展密切相关：（a）图像分析的代数化；（b）接受空间信息作为输入数据的图像识别算法；（c）多个分类器（MAC）。为了提供以下可能性，引入了新的一类图像分析模型：（a）标准化，建模和优化描述算法方案（DAS），这些算法构成MMCAI的大脑，并处理异构的结构不良信息-对偶表示形式–初始数据的空间，符号和数字表示形式；（b）比较分析，标准化，建模和优化用于分析和识别空间信息的不同算法。