Abstract
This special issue of PRIA is devoted to some scientific results and trends of the 25th International Conference on Pattern Recognition (Virtual, Milano, Italy, January 10–15, 2021). Two important events of ICPR-2020 are represented in this special issue: (1) The paper of Professor Ching Yee Suen (Centre for Pattern Recognition and Machine Intelligence, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada)–the recent winner of IAPR very prestigious K.S. Fu Prize for a year of 2020. The paper based on his lecture “From handwriting to human personality and facial beauty” presented at the ICPR 2020; (2) Special issue “ICPR-2020 Workshop “Image Mining. Theory and Applications.” The analysis of the scientific contribution of IMTA-VII-2021 allows us to draw the following conclusions: (1) The construction of a unified mathematical theory of image analysis is still far from complete. (2) There is considerable interest in the development of new mathematical methods for analyzing and evaluating information presented in the form of images. (3) There is a tendency to expand the mathematical apparatus in the development of new methods of image analysis and recognition by involving in this process areas of mathematics that were not previously used in image analysis. (4) The gap between the capabilities of new mathematical methods of image analysis and recognition and their actual use in solving applied problems remains significant. (5) There is an excessive use of neural networks in solving applied problems of image analysis and image recognition, and quite often without proper justification and interpretation of the results. The special issue includes articles based on the workshop papers selected by the IMTA-VII-2021 Program Committee for publication in PRIA. The PRIA special issue “Scientific Resume of the 25th International Conference on Pattern Recognition” is prepared by the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Sciences, the IAPR member society, and by the IAPR Technical Committee no. 16 on Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis.
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This work was partially supported by the Russian Foundation for Basic Research (grant no. 20-07-01031).
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Igor’ B. Gurevich. Born on August 24, 1938. He graduated from the Moscow Power Engineering Institute in 1961 (Automatic Control and Electrical Engineering) and defended his Candidate’s Dissertation in Physics and Mathematics at the Moscow Institute of Physics and Technology in 1975. Leading Researcher at the Federal Research Center Computer Science and Control of the Russian Academy of Sciences. He works since 1960 till now as an engineer, researcher, and lecturer in industry, research institutions, medicine, and universities, and, since 1985, he works 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, and in automation of scientific research, and knowledge-based systems.
Gurevich suggested, proved, and developed with his pupils the descriptive approach to image analysis and recognition (DAIA). Within DAIA a new class of image algebra was introduced, defined, and investigated (descriptive image algebras); new types of image models were introduced, classified, and investigated; axioms of descriptive theory of image analysis were introduced; a common model of image recognition process was defined and investigated; new settings of image analysis and recognition problems were introduced; a notion “image equivalence” was introduced and investigated; new classes of image recognition algorithms were defined and investigated; an image formalization space was introduced, defined, and investigated.
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.
Gurevich is an author of 2 monographs, 307 papers in peer reviewed journals and proceedings indexed in Web of Science, Scopus, and Russian Science Citation Index on the platform of Web of Science, and 31 invited papers at international conferences and is a 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.
He is vice-chairman of the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Sciences, Member of the International Association for Pattern Recognition (IAPR) Governing Board (representative of Russia), and the IAPR Fellow. He has been the Primary Investigator of 63 R&D projects as part of national and international research programs. He is Vice-Editor-in-Chief of the Pattern Recognition and Image Analysis, the 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. He has teaching experience at the Lomonosov Moscow State University, Russia (Assistant Professor), Dresden Technical University, Germany (Visiting Professor), George Mason University, USA (Research Fellow). He supervised of 6 PhD students and many graduate and master students.
Vera V. Yashina. Born September 13, 1980. Diploma Mathematician, Lomonosov Moscow State University (2002). Cand. Sci. (Phys.–Math.) (Theoretical Foundations of Informatics), 2009, Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow. Leading Researcher at the Department for Recognition, Security, and Analysis of Information at the Federal Research Center Computer Science and Control of the Russian Academy of Sciences. She works in the Russian Academy of Sciences since 2001. 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 biomedical image analysis.
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 and Membership Committees of the International Association for Pattern Recognition. She is a Vice Chair of Technical Committee no. 16 on Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis 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, an international journal of the RAS. Author of 79 papers in peer reviewed journals, conference and workshop proceedings. Web of Science: 11 papers; Hirsh index is 4; SCOPUS: 40 papers, 162 citations in 75 papers; Hirsh index is 8; Russian Science Citation Index on the platform of Web of Science: 56 papers; 255 citations; Hirsh index is 9. She was awarded several times for the best young scientist papers presented at the international conferences. Teaching experience: Lomonosov Moscow State University. She supervised several graduate and master students.
Davide Moroni received the MSc degree (Hons.) in Mathematics from the University of Pisa, in 2001, the Diploma from the Scuola Normale Superiore of Pisa, in 2002, and the PhD degree in mathematics from the University of Rome La Sapienza, in 2006. He is a Researcher with the Institute of Information Science and Technologies (ISTI), National Research Council, Pisa, Italy. He is currently the Head of the Signals and Images Lab, ISTI. He is the Chair of the MUSCLE working group (https://wiki.ercim.eu/wg/MUSCLE) of the European Consortium for Informatics and Mathematics. Since 2018, he serves as the Chair of the Technical Committee 16 on Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis (http://iapr-tc16.eu) of the International Association for Pattern Recognition (IAPR). He is an Associate Editor of IET Image Processing. His main research interests include geometric modeling, computational topology, image processing, computer vision, and medical imaging. At the moment, he is leading the CNR-ISTI team in the National Project PON MIUR S4E, working on maritime safety and security, and in the regional Project IRIDE addressing AR technologies and computer vision of Industry 4.0.
Maria Antonietta Pascali received her MSc in Mathematics honours degree from the University of Pisa in 2005, PhD in Mathematics at the University of Rome “La Sapienza” in 2010. She is a Researcher at CNR in Pisa since February 1, 2010. Member of IAPR TC16 on Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis. Research interests: modeling the protein 3D motion, 3D virtual environment in cultural heriitage, heterogeneous and multimodal data integration for underwater archaeology; 3D shape analysis for e-health, thermal imaging, statistical analysis of health-related data, applied computational topology; interplay of topological data analysis and artificial intelligence; deep learning applied to mp-MRI images.
LIST OF PAPERS INCLUDED INTO THE SPECIAL ISSUE
LIST OF PAPERS INCLUDED INTO THE SPECIAL ISSUE
Invited Papers
1. I. B. Gurevich and V. V. Yashina, “Descriptive models of information transformation processes in image analysis.”
2. Davide Moroni and Maria Antonietta Pascali, “Learning topology: Bridging computational topology and machine learning.”
3. Bernd Radig, Paul Bodesheim, Dimitri Korsch, Joachim Denzler, Timm Haucke, Morris Klasen, and Volker Steinhage, “Automated visual large scale monitoring of faunal biodiversity.”
Contributed Papers
4. N. A. Andriyanov, “Application of computer vision systems for monitoring the condition of drivers based on facial image analysis.”
5. N. A. Andriyanov, V. E. Dementev, K. K. Vasiliev, and A. G. Tashlinskii, “Investigation of methods for increasing the efficiency of convolutional neural networks in identifying tennis players.”
6. V. E. Antsiperov, “Representation of images by the optimal lattice partitions of samples.”
7. P. A. Chochia, “Image decomposition algorithm with a structural constraint of the averaging region.”
8. V. E. Dementev, M. N. Suetin, and M. A. Gaponova, “Using machine learning techniques to detect defects in images of metal structures.”
9. S. D. Dvoenko, “A developing of the Kemeny median: New types and algorithms.”
10. I. B. Gurevich, M. V. Budzinskaya, V. V. Yashina, A. M. Nedzved, A. T. Tleubaev, V. G. Pavlov, and D. V. Petrachkov, “A new method for automating the diagnostic analysis of human fundus images obtained using optical coherent tomography angiography.”
11. N. Yu. Ilyasova, A. S. Shirokanev, and N. S. Demin, “Development of high-performance algorithms for the segmentation of fundus images using a graphics processing unit.”
12. A. N. Karkishchenko and V. B. Mnukhin, “Reduced sign representations for characteristic points selection in images.”
13. V. R. Krashennikov, Yu. E. Kuvaiskova, O. E. Malenova, and A. Yu. Subbotin, “Testing hypotheses about covariance functions of cylindrical and circular images.”
14. Eckart Michaelsen, “Explorations on the depth of gestalt hierarchies in social imagery.”
15. E. V. Myasnikov, “Comparison of spectral dissimilarity measures and dimension reduction techniques for hyperspectral images.”
16. Rodrigo Nava, Duc Fehr, Frank Petry, and Thomas Tamisier, “Tire surface segmentation in infrared imaging with convolutional neural networks and transfer learning.”
17. Ghulam-Sakhi Shokouh, Baptiste Magnier, Binbin Xu, and Philippe Montesinos, “Ridge detection by image filtering techniques: A review and an objective analysis.”
18. S. A. Usilin, O. A. Slavin, and V. V. Arlazarov, “Memory consumption and computation efficiency improvements of Viola–Jones object detection method for remote sensing applications.”
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Gurevich, I.B., Moroni, D., Pascali, M.A. et al. On Some Scientific Results of the ICPR-2020. Pattern Recognit. Image Anal. 31, 357–363 (2021). https://doi.org/10.1134/S1054661821030093
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DOI: https://doi.org/10.1134/S1054661821030093