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Descriptive image analysis: Genesis and current trends

  • Mathematic Theory of Pattern Recognition
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Abstract

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 designing a unified scheme for representation of objects under recognition and its transforms in the form of certain algebraic structures. This makes it possible to develop the corresponding regular structures ready for analysis by algebraic, geometrical, and topological techniques. The development of this line of image analysis and pattern recognition is of crucial importance for automatic image mining and application problems solving, in particular, for diversification of the classes and types of solvable problems, as well as for significant improvement of the efficiency and quality of solutions. The main subgoals of the paper are (1) to set forth the-state-of-the-art of the mathematical theory of image analysis; (2) to consider the algebraic approaches and techniques suitable for image analysis; and (3) to present a methodology, as well as mathematical and computational techniques, for automation of image mining on the basis of the descriptive approach to image analysis (DAIA). The main trends and problems in the promising basic researches focused on the development of a descriptive theory of image analysis are described.

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  1. Federal Research Center Computer Science and Control, Russian Academy of Sciences, ul. Vavilova 44/2, Moscow, 119333, Russia

    I. B. Gurevich & V. V. Yashina

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  1. I. B. Gurevich
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Igor’ В. Gurevich. Born August 24, 1938. Dr.-Eng. diploma engineer (automatic control and electrical engineering), 1961, National Research University “Moscow Power Engineering Institute, Moscow, USSR; Dr. (mathematical cybernetics), 1975, Moscow Institute of Physics and Technology (State University), Moscow, USSR. Head of the Department “Mathematical and Applied Problems of Image Analysis” at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, the Russian Federation. He has worked from 1960 till now as an engineer and researcher in industry, medicine, and universities, and from 1985 in the USSR/Russian Academy of Sciences. Area of expertise: mathematical theory of image analysis, image-mining, image understanding, mathematical theory of pattern recognition, theoretical computer science, medical informatics, applications of pattern recognition and image analysis techniques in medicine and in automation of scientific research, and knowledge-based systems. Author of 2 monographs and of 290 papers in peer reviewed journals and proceedings, 30 invited papers at international conferences, holder of 6 patents. Vice-Chairman of the National Committee for Pattern Recognition and Image Analysis of the Presidium of the Russian Academy of Sciences, Member of the International Association for Pattern Recognition (IAPR) Governing Board (representative from RF), IAPR Fellow. He has been the PI of 62 R&D projects as part of national and international research programs. Vice-Editor-in-Chief of the “Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications” international journal of the RAS, member of editorial boards of several international scientific journals, member of the program and technical committees of many international scientific conferences. Teaching experience: Moscow State University, RF (assistant professor), Dresden Technical University, Germany (visiting professor), George Mason University, USA (visiting professor). He was supervisor of 6 PhD students and many graduate and master students.

Vera V. Yashina. Born September 13, 1980. Diploma mathematician, Moscow State University (2002). Dr. (Theoretical Foundations of Informatics), 2009, Dorodnicyn Computing Center of the Russian Academy of Sciences. Leading researcher at the Department “Mathematical and Applied Problems of Image Analysis” at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, the Russian Federation. She has worked from 2001 until now in the Russian Academy of Sciences. Scientific expertise: mathematical theory of image analysis, image algebras, models and medical informatics. She is scientific secretary of the National Committee for Pattern Recognition and Image Analysis of the Presidium of the Russian Academy of Sciences. She is a member of the Educational Committee of the International Association for Pattern Recognition. She has been the member of many R&D projects as part of national and international research programs. Member of editorial board of “Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications” international journal of the RAS. Author of 66 papers in peer reviewed journals, conference and workshop proceedings. She was awarded several times for the best young scientist papers presented at the international conferences. Teaching experience: Moscow State University, RF. She was supervisor of several graduate and master students.

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Gurevich, I.B., Yashina, V.V. Descriptive image analysis: Genesis and current trends. Pattern Recognit. Image Anal. 27, 653–674 (2017). https://doi.org/10.1134/S1054661817040071

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