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.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
C. M. Ablow and D. J. Kaylor, “Inconsistent homogeneous linear inequalities, Bull. Am. Math. Soc. 71 (5), 724 (1965).
H. G. Barrow, A. P. Ambler, and R. M. Burstall, “Some techniques for recognizing structures in pictures,” in Proc. Int. Conf. on Frontiers of Pattern Recognition, Ed. by Satosi Watanabe (Academ. Press, New York, London, 1972), pp. 1–30.
G. Birkhoff and J. D. Lipson, “Heterogeneous algebras,” J. Combinat. Theory 8, 115–133 (1970).
V. M. Chernov, “Clifford algebras are group algebras projections,” in Advanches in Geometric Algebra with Applications in Science and Engineering, Ed. by E. Bayro-Corrochano and G. Sobczyk (Birkhauser, Boston, 2001), pp. 467–482.
V. M. Chernov, “On defining equations for the elements of associative and commutative algebras,” in Space-Time Structure. Algebra and Geometry, Ed. by D. Pavlov, Gh. Atanasiu, and V. Balan (Lilia Print, 2007), pp. 182–188.
J. Crespo, J. Serra, and R. W. Schaffer, “Graph-based morphological filtering and segmentation,” in Proc. 6th Symp. on Pattern Recognition and Image Analysis (Cordoba, 1995), pp. 80–87.
J. L. Davidson, “Classification of lattice transformations in image processing,” Comput. Vision, Graph., Image Processing: Image Understand. 57 (3), 283–306 (1993).
M. J. B. Duff, D. M. Watson, T. J. Fountain, and G. K. Shaw, “A cellular logic array for image processing,” Pattern Recogn. 5 (3), 229–247 (1973).
E. R. Dougherty and D. Sinha, Computational Gray-Scale Mathematical Morphology on Lattices (A Comparator-Based Image Algebra), Part 1: Architecture. Real-Time Imaging (Acad. Press, 1995), Vol. 1, pp. 69–85.
E. R. Dougherty and D. Sinha, Computational Gray- Scale Mathematical Morphology on Lattices (A Comparator- based Image Algebra), Part 2: Image Operators. Real-Time Imaging (Acad. Press, 1995), Vol. 1, pp. 283–295.
E. R. Dougherty, “A homogeneous unification of image algebra. Part I: The homogenous algebra,” Imaging Sci. 33 (4), 136–143 (1989).
E. R. Dougherty, “A homogeneous unification of image algebra. Part II: Unification of image algebra,” Image Sci. 33 (4), 144–149 (1989).
T. G. Evans, “A formalism for the description of complex objects and ist implementation,” in Proc. 5th Int. Conf. on Cybernetics (Namur, Sept. 1967).
T. G. Evans, “Descriptive pattern analysis techniques: Potentialities and problems,” in Proc. Int. Conf. on Methodologies of Pattern Recognition (Acad. Press, New York, London, 1969), pp. 149–157.
M. Felsberg, Th. Bulov, G. Sommer, and V. M. Chernov, “Fast algorithms of hypercomplex Fourier transforms,” in Geometric Computing with Clifford Algebras, Ed. by G. Sommer (Springer Verlag, 2000), pp. 231–254.
Ya. A. Furman, “Parallel recognition of different classes of patterns,” Pattern Recogn. Image Anal. 19 (3), 380–393 (2009).
Ya. A. Furman, “Recognition of vector signals represented as a linear combination,” J. Commun. Technol. Electron. 55 (6), 627–638 (2010).
Ya. A. Furman and I. L. Egoshina, “Inverse problem of rotation of three-dimensional vector signals,” Optoelectron. Instrumentat. Data Processing 46 (1), 37–45 (2010).
Ya. A. Furman, R. V. Eruslanov, and I. L. Egoshina, “Recognition of images and recognition of polyhedral objects,” Pattern Recogn. Image Anal. 22 (1), 196–209 (2012).
P. D. Gader, M. A. Khabou, and A. Koldobsky, “Morphological regularization neural networks,” Pattern Recogn. 33, 935–944 (2000).
U. Grenander, Lectures in Pattern Theory (Sprinder-Verlag, New York, 1976–1981).
U. Grenander, General Pattern Theory. A Mathematical Study of Regular Structure (Clarendon Press, Oxford, 1993).
U. Grenander, Elements of Pattern Theory (Johns Hopkins Univ. Press, 1996).
J. Grin, J. Kittler, P. Pudil, and P. Somol, “Information analysis of multiple classifier fusion,” in Proc. 2nd Int. Workshop on Multiple Classifier Systems, MCS 2001, Cambridge, UK, July 2001 (Springer-Verlag, 2001), pp. 168–177.
I. B. Gurevich, “Algebraic approach for pattern recognition and analysis,” in Proc. All-Union Conf. Mathematical Methods for Pattern Recognition (Dilizhan, May 16–21, 1985) (Armenian SSR Acad. Sci., Erevan, 1985), p. 55[in Russian].
I. B. Gurevich, “The descriptive framework for an image recognition problem,” in Proc. 6th Scandinavian Conf. on Image Analysis (Pattern Recognition Soc. of Finland, 1989), Vol. 1, pp. 220–227.
I. B. Gurevich, “Descriptive technique for image description, representation and recognition,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. USSR 1, 50–53 (1991).
I. B. Gurevich, “The descriptive approach to image analysis. Current state and prospects,” in Proc. 14th Scandinavian Conf. on Image Analysis (Springer-Verlag, Berlin, Heidelberg, 2005), pp. 214–223.
I. B. Gurevich and I. A. Jernova, “The joint use of image equivalents and image invariants in image recognition,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 13 (4), 570–578 (2003).
I. B. Gurevich and I. V. Koryabkina, “Comparative analysis and classification of features for image models,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 16 (3), 265–297 (2006).
I. B. Gurevich and V. V. Yashina, “Descriptive image algebras with one ring,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 13 (4), 579–599 (2003).
I. B. Gurevich and V. V. Yashina, “Algorithmic scheme based on a descriptive image algebra with one ring: image analysis example,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 15 (1), 192–194 (2005).
I. B. Gurevich and V. V. Yashina, “Generating descriptive trees,” in Proc. 10th Int. Fall Workshop on Vision, Modeling, and Visualization (2005), pp. 367–374.
I. B. Gurevich and V. V. Yashina, “Operations of descriptive image algebras with one ring,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 16 (3), 298–328 (2006).
I. B. Gurevich and V. V. Yashina, “Computer-aided image analysis based on the concepts of invariance and equivalence,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 16 (4), 564–589 (2006).
I. B. Gurevich and V. V. Yashina, “Descriptive approach to image analysis: image models,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 18 (4), 518–541 (2008).
I. Gurevich and V. Yashina, “Image formalization via descriptive image algebras,” in Proc. 3rd Int. Workshop on Image Mining Theory and Applications–IMTA 2010 (in Conjunction with VISIGRAPP 2010), Angers, France, May 2010, Ed. by I. Gurevich, H. Niemann, and O. Salvetti (INSTICC Press, 2010), pp. 19–28.
I. B. Gurevich and V. V. Yashina, “Image formalization space: Formulation of tasks, structural properties, and elements,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 21 (2), 134–139 (2011).
I. B. Gurevich and V. V. Yashina, “Descriptive approach to image analysis: Image formalization space,” Pattern Recogn. Image Anal. 22 (4), 495–518 (2012).
I. Gurevich, Yu. Trusova, and V. Yashina, “Current trends in the algebraic image analysis. A survey,” in Proc. 18th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013, Havana, Cuba, Nov. 20–23, 2013, Ed. by J. Ruiz-Shulcloper and G. Sanniti di Baja (Springer-Verlag, Berlin, Heidelberg, 2013), Ch. 1, pp. 423–430.
I. B. Gurevich and Yu. I. Zhuravlev, “Computer science: subject, fundamental research problems, methodology, structure, and applied problems,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 24 (3), 333–346 (2014).
I. B. Gurevich, Yu. I. Zhuravlev, A. A. Myagkov, Yu. O. Trusova, and V. V. Yashina, “On basic problems of image recognition in neurosciences and heuristic methods for their solution,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 25 (1), 132–160 (2015).
H. Hadwiger, “Uber Treffanzahlen bei translationsgleichen Eikorpern,” Arch. Math. 8, 212–213 (1957).
R. Haralick and L. Shapiro, “Image segmentation techniques,” Comput. Vision, Graph., Image Processing 29, 100–132 (1985).
R. Haralick, L. Shapiro, and J. Lee, “Morphological edge detection,” IEEE J. Robot. Automat. RA-3 (1), 142–157 (1987).
R. M. Haralick, S. R. Sternberg, and X. Zhuang, “Image analysis using mathematical morphology,” IEEE Trans. Pattern Anal. Machine Intellig. PAMI-9 (4), 532–550 (1987).
Joo Hyoman, R. N. Haralick, and L. G. Shapiro, “Toward the automatic generation of mathematical morphology procedures using predicate logic,” in Proc. 3rd Int. Conf. on Computer Vision (Osaka, 1990), pp. 156–165.
S. Kaneff, “Pattern cognition and the organization of information,” in Proc. Int. Conf. on Frontiers of Pattern Recognition, Ed. by Satosi Watanabe (Acad. Press, New York, London, 1972), pp. 193–222.
M. Yu. Khachai, “On the computational complexity of the minimum committee problem,” J. Math. Model. Algor. 6 (4), 547–561 (2007).
M. Yu. Khachai, “Computational complexity of recognition learning procedures in the class of piecewiselinear committee decision rules,” Automat. Remote Control 71 (3), 528–539 (2010).
R. Kirsh, “Computer interpretation of english text and picture patterns,” IEEE-TEC EC-13 (4) (1964).
J. Kittler and F. M. Alkoot, “Relationship of sum and vote fusion strategies,” in Proc. 2nd Int. Workshop on Multiple Classifier Systems MCS 2001 Cambridge, UK, July 2001 (Springer-Verlag, 2001), pp. 339–348.
Applications of Discrete Geometry and Mathematical Morphology, Ed. by U. Köthe, A. Montanvert, and P. Soille (Lecture Notes in Computer Science, 2012), Vol. 7346.
E. N. Kuzmin, “On the Nagata-Higman theorem,” in Proc. Dedicated to the 60th Birthday of Academician L. Iliev Mathematical Structures–Computational Mathematics–Mathematical Modeling (Sofia, 1975), pp. 101–107.
V. G. Labunetc, Algebraic Theory of Signals and Systems (Digital Signal Processing) (Krasnoyarsk Univ., Krasnoyarsk, 1984).
J. S. J. Lee, R. M. Haralick, and L. G. Shapiro, “Morphologic edge detection,” IEEE J. Robot. Automat. RA-3 (2), 142–156 (IEEE, 1987).
D. A. Leites, “Introduction to the theory of supermanifolds,” Russ. Math. Survey 35 (1), 1–64 (1980).
Yu. N. Maltsev and E. N. Kuzmin, “A basis for identities of the algebra of second order matrices over a nite eld,” Algebra Logic 17, 17–21 (1978).
A. I. Malcev, Algebraic Systems (Nauka, Moscow, 1970; Springer-Verlag, Berlin, 1973).
V. L. Matrosov, “Pair isomorphism of permissible objects in recognition problems,” USSR Comput. Math. Math. Phys. 23 (1), 123–127 (1983).
V. L. Matrosov, “On the incompleteness of a model of algorithms for computing estimates,” USSR Comput. Math. Math. Phys. 23 (2), 128–136 (1983).
V. L. Matrosov, “Lower bounds of the capacity of L-dimensional algebras of estimatecomputing algorithms,” USSR, Comput. Math. Math. Phys. 24 (6), 182–188 (1984).
V. L. Matrosov, “The capacity of polynomial expansions of a set of algorithms for calculating estimates,” USSR Comput. Math. Math. Phys. 25 (1), 79–87 (1985).
G. Matheron, Random Sets and Integral Geometry (Wiley, New York, 1975).
P. Maragos, “Algebraic and PDE approaches for lattice scale-spaces with global constraints,” Int. J. Comput. Vision 52 (2/3), 121–137 (2003).
P. Maragos and R. Schafer, “Morphological skeletons representation and coding of binary images,” IEEE Trans. Acoustics, Speech, Signal Processing 34 (5), 1228–1244 (1986).
P. Maragos and R. W. Schafer, “Morphological filters. Part I: their set-theoretic analysis and relations to linear shift-invariant filters,” IEEE Trans. Acoustics, Speech, Signal Processing ASSP-35, 1153–1169 (1987).
P. Maragos and R. W. Schafer, “Morphological filters. Part II: Their relations to median, order-statistic, and stack filters,” IEEE Trans. Acoustics, Speech, Signal Processing ASSP-35, 1170–1184 (1987).
D. Marr, Vision (Freeman, New York, 1982).
V. D. Mazurov, “A committee of a system of convex inequalities,” Siberian Math. J. 9 (2), 354–357 (1998).
V. D. Mazurov, “Committees of inequalities systems and the recognition problem,” Cybernetics 7 (3), 559–567 (1971).
V. D. Mazurov and M. Yu. Khachai, “Committees of systems of linear inequalities,” Automat. Remote Control 65 (2), 193–203 (2004).
V. D. Mazurov and M. Yu. Khachai, “Parallel computations and committee constructions,” Automat. Remote Control 68 (5), 912–921 (2007).
P. Miller, “Development of a mathematical structure for image processing,” Optical Division Tech. Report (Perkin-Elmer, 1983).
H. Minkowski, Geometrie der Zahlen (Teubner, Leipzig, 1896/1910).
R. Narasimhan, “Syntax-directed interpretation of classes of pictures,” Commun. ACM 9 (3) (1966).
R. Narasimhan, “Labeling schemata and syntactic descriptions of pictures,” Inf. Control 7 (2) (1967).
R. Narasimhan, “On the description, generalization and recognition of classes of pictures,” in Proc. NATO Summer School on Automatic Interpretation and Classification of Images (Pisa, Aug. 26–Sept. 7, 1968).
R. Narasimhan, “Picture languages,” in Picture Language Machines, Ed. by S. Kaneff (Acad. Press, London, New York, 1970), pp. 1–30.
J. von Neumann, “The general logical theory of automata,” in Proc. Hixon Symp. Celebral Mechenism in Behavior (John Wiley and Sons, New York, 1951).
J. von Neumann, Theory of Self-Reproducing Automata (Univ. of Illinois Press, Urbana, IL, 1966).
M. Pavel, “Pattern recognition categories,” Pattern Recogn. 8 (3), 115–118 (1976).
M. Pavel, Fundamentals of Pattern Recognition (Marcell Dekker, New York, 1989).
Yu. P. Pytiev, Method of Mathematical Modeling of Measuring and Computing Systems, 2nd ed. (MAIK Nauka, Moscow, 2004)[in Russian].
B. Radunacu, M. Grana, and F. X. Albizuri, “Morphological scale spaces and associative morphological memories: results on robustness and practical applications,” J. Math. Imag. Vision 19, 113–131 (2003).
G. X. Ritter, P. Sussner, and J. L. Diaz-de-Leon, “Morphological associative memories,” IEEE Trans. Neural Networks 9 (2), 281–292 (1998).
G. X. Ritter and P. Sussner, “Introduction to morphological neural networks,” in Proc. ICPR 1996 (IEEE, 1996), pp. 709–716.
G. X. Ritter, J. L. Diaz-de-Leon, and P. Sussner, “Morphological bidirectional associative memories,” Neural Networks 12, 851–867 (1999).
G. X. Ritter and J. N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, 2d ed. (CRC Press, 2001).
G. X. Ritter, Image Algebra (Center for Computer Vision and Visualization, Dep. Computer and Information Science and Engineering, Univ. of Florida, Gainesville, FL, 2001).
G. X. Ritter and P. D. Gader, “Image algebra techniques for parallel image processing,” Parallel Distribut. Comput. 4 (5), 7–44 (1987).
G. X. Ritter, J. N. Wilson, and J. L. Davidson, “Image algebra: an overview,” Comput. Vision, Graph., Image Processing 49, 297–331 (1990).
A. Rosenfeld, Picture Languages. Formal Models for Picture Recognition (Acad. Press, New York, San Francisco, London, 1979).
A. Rosenfeld, “Digital topology,” Am. Math. Monthly 86, 621–630 (1979).
K. V. Rudakov, “Universal and local constraints in the problem of correction of heuristic algorithms,” Cybernetics 23 (2), 181–186 (1987).
K. V. Rudakov, “Completeness and universal constraints in the correction problem for heuristic classification algorithms,” Cybernetics 23 (3), 414–418 (1987).
K. V. Rudakov, “Symmetric and functional constraints in the correction problem of heuristic classification algorithms,” Cybernetics 23 (4), 528–533 (1987).
K. V. Rudakov, “Application of universal constraints in the analysis of classification algorithms,” Cybernetics 24 (1), 1–6 (1988).
K. V. Rudakov and K. V. Vorontsov, “Methods of optimization and monotone correction in the algebraic approach to the recognition problem,” Dokl. Math. 60, 139–142 (1999).
K. V. Rudakov and Yu. V. Chekhovich, “Completeness criteria for classification problems with set-theoretic constraints,” Comput. Math. Math. Phys. 45 (2), 329–337 (2005).
K. V. Rudakov, A. A. Cherepnin, and Yu. V. Chekhovich, “On metric properties of spaces in classification problems,” Dokl. Math. 76 (2), 790–793 (2007).
K. V. Rudakov and I. Yu. Torshin, “Selection of informative feature values on the basis of solvability criteria in the problem of protein secondary structure recognition,” Dokl. Math. 84 (3), 871–874 (2011).
J. Serra, Image Analysis and Mathematical Morphology (Acad. Press, London, 1982).
J. Serra, “Morphological filtering: an overview,” Signal Processing 38 (1), 3–11 (1994).
J. Serra, “Introduction to morphological filters,” in Image Analysis and Mathematical Morphology, Vol. 2: Theoretical Advances, Ed. by J. Serra (Acad. Press 1998), Ch. 5, pp. 101–114.
I. N. Sinicyn, Calman and Pugachev Filters, 2nd ed. (Logos, Moscow, 2007)[in Russian].
A. Shaw, “A proposed language for the formal description of pictures,” CGS Memo (Stanford Univ., 1967), no. 28.
A. Shaw, “The formal description and parsing of pictures,” Ph.D. Thesis (Computer Sciences Department, Stanford Univ., Dec. 1967); Tech. Rept CS94 (Apr. 1968).
M. Schlesinger and V. Hlavac, Ten Lectures on Statistical and Structural Pattern Recognition, Vol. 24: Computational Imaging and Vision (Kluwer Acad. Publ., Dordrecht, Boston, London, 2002).
P. Soille, “Morphological partitioning of multispectral images,” J. Electron. Imag. 5 (3), 252–265 (1996).
P. Soille, Morphological Image Analysis. Principles and Applications, 2nd ed. (Springer-Verlag, Berlin, Heidelberg, New York, 2003, 2004).
S. R. Sternberg, “Language and architecture for parallel image processing,” in Proc. Conf. on Pattern Recognition in Practice (Amsterdam, 1980).
S. R. Sternberg, “An overview of image algebra and related architectures,” in Integrated Technology for Parallel Image Processing, Ed. by S. Levialdi (Academic Press, London, 1985).
S. R. Sternberg, “Grayscale morphology,” Comput. Vision, Graphics Image Processing 35, (3), 333–355 (1986).
P. Sussner, “Observations on morphological associative memories and the kernel method,” Neurocomputing 31, 167–183 (2000).
P. Sussner and G. X. Ritter, “Rank-based decompositions of morphological templates,” IEEE Trans. Image Processing 09 (8), 1420–1430 (2000).
P. Sussner, “Generalizing operations of binary autoassociative morphological memories using fuzzy set theory,” J. Math. Imag. Vision 19, 81–93 (2003).
D. M. J. Tax and R. P. W. Duin, “Combining oneclass classifiers,” in Proc. 2nd Int. Workshop on Multiple Classifier Systems MCS 2001, Cambridge, UK, July 2001 (Springer-Verlag, 2001).
A. Toet, “A morphological pyramidal image decomposition,” Pattern Recogn. Lett. 9, 255–261 (1989).
S. H. Unger, “A computer oriented toward spatial problems,” Proce. IRE 46, 1744–1750 (1958).
B. L. Van Der Waerden, Algebra I, Algebra II (Springer-Verlag, Berlin, Heidelberg, New York, 1971).
D. Winbridge and J. Kittler, “Classifier combination as a tomographic process,” in Proc. 2nd Int. Workshop on Multiple Classifier Systems, MCS 2001, Cambridge, UK, July 2001 (Springer-Verlag, 2001), pp. 248–258.
Yu. I. Zhuravlev, “On algebraic approach for solving the recognition and classification problems,” in Problems of Cybernetics (Nauka, Moscow, 1978), Issue 33[in Russian].
Yu. I. Zhuravlev, “An algebraic approach to recognition and classification problems,” Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 8, 59–100 (1998).
Author information
Authors and Affiliations
Corresponding author
Additional information
The text was submitted by the authors in English.
Supplementary materials are available for this article at 10.1134/S1054661817040071 and are accessible for authorized users
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.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
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
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661817040071