Image Analysis And Pattern Recognition: State Of The Art In The Russian Federation: 7 (Series on Language Processing, Pattern Recognition, and Intelligent Systems)

This collective compendium highlights the achievements of Soviet and Russian mathematical and computer science scientific schools in the area of image analysis and understanding, pattern recognition, artificial intelligence and adjacent fields of computer sciences and applied mathematics.

Contributed by renowned researchers, the materials collated are original papers never published before. This chapters provide good balance between fundamental and applied statements of problems and results.

This unique reference text benefits professionals, researchers, academics, and graduate students in pattern recognition/image analysis, theoretical computer science and AI.

Igor B 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 (National Research University), Moscow, USSR. Leading Researcher 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, researcher and lecturer in industry, research institutions, medicine, 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 biology, medicine, in automation of scientific research, and knowledge-based systems.

I B Gurevich suggested, proved and developed with his pupils Descriptive Approach to Image Analysis and Recognition (DAIA). In DAIA framework a new class of image algebras was introduced, defined and studied (Descriptive Image Algebras); new types of image models were introduced, classified and studied; axioms of Descriptive Theory of Image Analysis were introduced; a common model of image recognition process was defined and stufied; new settings of image analysis and recognition problems were introduced; a notion "image equivalence" was introduced and studied; new classes of image recognition algorithms were defined and studited; an image formalization space was introduced, defined and studied.

Listed results were used in development of software kits for image analysis and recognition and for solution of important and difficult applied problems of automated bio-medical image analysis.

I B Gurevich is an author of 2 monographs and of 310 papers in peer reviewed journals and conference proceedings indexed in Web of Science, Scopus and Russian Science Citation Index on the platform of Web of Science, 33 invited papers at international conferences, holder of 8 patents. Web of Science: 22 papers; SCOPUS: 76 papers, 287 citations in 148 documents; Hirsh index is 10; Russian Science Citation Index on the platform of Web of Science: 129 papers; 910 citations; Hirsh index is 11.

Vera V Yashina born 13.09.1980. Diploma mathematician, Moscow Lomonosov State University (2002). Dr (Theoretical Foundations of Informatics), 2009, Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow. Leading researcher at the Department "Recognition, security and analysis of information" 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.

The main results were obtained in mathematical theory of image analysis: descriptive image algebras with one ring were defined, classified and investigated; a new topological image formalization space was specified and investigated; descriptive generating trees were defined, classified and investigated. Listed results were applied in bio-medical image analysis.