A classification algorithm based on the complete decision tree

Application of decision trees in problems of classification by precedents is considered. A new model of classifier (which is called the complete decision tree) is proposed and compared with other recognition algorithms based on constructing decision trees. Elena V. Djukova born 1945. Graduated from Moscow State University in 1967. Candidate’s degree in Physics and Mathematics in 1979. Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of 76 papers. Nikolai V. Peskov born 1978. Graduated from Moscow State University in 2000. Candidate’s degree in Physics and Mathematics in 2004. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of seventeen papers.     

Increasing the efficiency of combinatorial logical data analysis in recognition and classification problems

Problems of increasing the efficiency of combinatorial logical data analysis in recognition problems are examined. A technique for correct conversion of initial information for reduction of its dimensionality is proposed. Results of testing this technique for problems of real medical prognoses are given. Djukova Elena V. Born 1945. Graduated from Moscow State University in 1967. Candidate’s degree in Physics and Mathematics in 1979. Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of 70 papers. Peskov Nikolai V. Born 1978. Graduated from Moscow State University in 2000. Candidate’s degree in 2004. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Inyakin Andrey S. Born 1978. Graduated from Moscow State University in 2000. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Sakharov Aleksei A. Born 1980. Graduated from Moscow State University in 2003. Moscow Pedagogical University, graduate student. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of three papers.     

Classifying the subjects of the Russian Federation on the basis of analysis of results of Gallup polling

The problem of processing of Gallup poll results by cluster analysis methods is considered. The aim of these polls, performed in different subjects of the Russian Federation, is to extract main characteristics of the regions. Demyanov Egor A. Born 1982. Graduated from the Moscow State University in 2004. Post-graduate student of the same university. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of two publications. Djukova Elena V. Born 1945. Graduated from the Moscow State University in 1967. Received candidate’s degree in Physics and Mathematics in 1979, Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of 76 papers. Peskov Nikolai V. Born 1978. Graduated from the Moscow State University in 2000. Received candidate’s degree in Physics and Mathematics in 2004. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of 17 papers. Inyakin Andrey S. Born 1978. Graduated from the Moscow State University in 2000. Received candidate’s degree in 2006. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of 16 papers.     

Decision methods for some discrete extreme problems in recognition theory

By optimizing different models of recognition algorithms, a number of discrete extreme problems appear. The search for the maximum solvable subsystem of the system of linear inequalities is one of these tasks. The solution algorithm for this problem is described. This algorithm is effective for linear systems of small ranks. Also, an approximate method that is effective for systems of large dimensionality is proposed. The text was submitted by the author in English. Natalja N. Katerinochkina. Born 1945. Graduated from the Faculty of Mechanics and Mathematics, Moscow State University, in 1967. Received candidates degree in Physics and Mathematics in 1978. The senior scientific worker at the Dorodnicyn Computing Centre, Russian Academy of Science. Scientific interests: discrete mathematics, mathematical cybernetics, pattern recognition, discrete optimization. Author of 35 publications.     

On the uni- and bimodality of a two-component Gaussian mixture

Several sufficient conditions are formulated for the uni- and bimodality of a mixture of two Gaussian distributions with equal variances σ² and different expectation values μ _i , i = 1, 2. An equation governing all the degenerate critical inflection points for the probability density f(x) of the mixture is derived by a statistical method. This equation describes the boundary of the uni- and bimodality domains of f(x). Nina Nikolaevna Aprausheva was born in 1940. Graduated from the Moscow State University (Department of Mechanics and Mathematics) in 1966. In 1982, defended the Cand. Sci. thesis “On the Application of Mixtures of Normal Distributions in Pattern Recognition.” Senior scientist at Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: mathematical statistics, computational mathematics, mathematical methods in pattern recognition, and decision theory (about 75 publications). Member of Association of Pattern Recognition and Association of. “Women in science and education.” N.N. Aprausheva’s publications have been awarded several prizes at the scientific publications contests in Computing Center of Russian Academy of Sciences. Stanislav Viktorovich Sorokin was born in 1947. Graduated from Moscow State University (Department of Computational Mathematics and Cybernetics) in 1971. Programmer at Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: computational mathematics, system programming, and pattern recognition (20 publications).     

An algorithm for median filtering on the basis of merging of ordered columns

This paper presents algorithms for optimization of the median filtering of images. A median filtering algorithm on the basis of merging ordered columns of an image in the 3 × 3 window of a filter is developed, which allows us to perform the filtering on a personal computer in real time without use of additional hardware. This algorithm outperforms other median filtering algorithms in execution speed on a personal computer. Zalesky Boris Andreevich. Born 1953. Graduated from the Lomonosov Moscow University in 1979. Received the candidate’s degree in 1982 and doctoral degree in Physics and Mathematics in 1990. Leading researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of 81 papers. In 1987 awarded Leninskii Komsomol prize in the area of mathematics. Kravchonok Aleksandr Ivanovich. Born 1982. Graduated from the Belarussian State University in 2004. Junior researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of two papers. Lukashevich Pavel Vladimirovich. Born 1982. Graduated from the Moscow Institute of Physics and Technology in 2005. Junior researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of one paper.     

Bounds for the number of modes of the simplest Gaussian mixture

Several theorems on sufficient unimodality conditions are formulated for a sum of k normal distributions with the same variance and with different mean values μ _i , i = 1, ..., k, 2 ≤ k < ∞, taken with their a priori probabilities π_i. On the basis of these theorems, estimates for the lower and upper bounds for the mode numbers m are obtained for k ≥ 3 in the case when the mixture contains k* components, 2 ≤ k* < k, satisfying the unimodality conditions. Aprausheva Nina Nikolaevna (b. 1940). Graduated from the Moscow State University (Department of Mechanics and Mathematics) in 1966. In 1982, defended the Cand. Sci. thesis “On the Application of Mixtures of Normal Distributions in Pattern Recognition.” Senior scientist at Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: mathematical statistics, computational mathematics, mathematical methods in pattern recognition, and decision theory (about 70 publications). Member of Association of Pattern Recognition. Mollaverdi Naser (b. 1967). Graduated from the Isfahan University of Technology (Iran) in 1990. In 1995, graduated from the Educational University (Teheran, Iran). Third-year scholar at Moscow State University (Department of Computational Mathematics and Cybernetics). Scientific interests: computational mathematics, pattern recognition, programming, and statistics (12 publications). Sorokin Stanislav Viktorovich (b. 1947). Graduated from Moscow State University (Department of Computational Mathematics and Cybernetics) in 1971. Programmer at Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: computational mathematics, system programming, and pattern recognition (15 publications).     

Compensation for local defects in an image created using a triple-color photo technique

Problems connected with the creation of elements of technology for automated detection and the correction of image local defects obtained in the triple-color photo technique are considered. Automated procedures of detection and correction are developed. Procedures are implemented in software-tool and used in works on the reconstruction of S. M. Prokudin-Gorskii’s collection of photos taken in the early 20th century. Viktor Vasil’evich Minakhin. Born in 1948. Candidate of Sciences in physics and mathematics. Graduated from Faculty of Mechanics and Mathematics of Moscow State University in 1971 with a specialty in common control problems. Deputy Director at Restavrator-M Research and Restoration Center. Scientific interests include representations of noncommutatively algebraic structure, processing and analysis of images, image recognition, and architecture of databases. Author of 28 papers. Dmitrii Mikhailovich Murashov. Candidate of Sciences in engineering, associate professor. Born in 1958. Graduated from Moscow Aviation Institute in 1981 with a specialty in automatic control systems. He works at Dorodnitsyn Computing Center of the Russian Academy of Sciences, Moscow. Scientific interests include automatic control, processing and analysis of the images, and image recognition. Author of 50 papers. Yurii Pavlovich Davidov. Born in 1964. Graduated from Moscow Aviation Institute in 1987 with a specialty in radio engineering. 1989–1999 worked for the Scientific Council on the Complex Problem of Cybernetics at the Russian Academy of Sciences. Since 1996, he has worked at Restavrator-M Ltd. Scientific interests include the development of Image databases; development of software for 3D reconstruction and processing data of geodesic measurements; and software for color registration, color correction, and the removal of image defects. Dmitrii Abramovich Dimentman. Born in 1962. Graduated from Moscow State Historical-Archive Institute in 1990 with a specialty in historical archiving. Since 1979, he has worked at the State Historical Museum, Moscow, currently as the curator of author negatives in the arts department and leading researcher. Scientific interests include photo history and the study of photo document collections. Author of 20 papers.     

Using adaptive and intelligent methods in constructing of a model of seismic noise

In the paper, an attempt is made to develop an approach to analysis of seismic signals with the use of Data Mining techniques. A system for registering of signals of seismic noise is described. Algorithms for signal segmentation are proposed. Geppener Vladimir Vladimirovich. Born 1940. Graduated from the Leningrad Electrotechnical Institute in 1964. Received candidate’s degree (in Engineering) in 1969 and Doctoral degree (in Engineering) in 2000. Professor at the Chair of Mathematical Software and Computer Applications of the St. Petersburg Electrotechnical University. Scientific interests: systems of signal processing, methods of artificial intelligence, and pattern recognition theory. Author and coauthor of more than 150 scientific publications. Member of the Russian Association for Pattern Recognition and Image Analysis. Tristanov Aleksandr Borisovich. Born 1981. Graduated with honors from the Kamchatka State Technical University in 2003. Post-graduate student at the Kamchatka State Pedagogical University. Works as an Assistant Professor at the Kamchatka State Pedagogical University. Scientific interests: systems of digital signal processing, methods of artificial intelligence, and frequency-time analysis of signals. Author of 10 scientific publications. Firstov Pavel Pavlovich. Born 1941. Graduated with honors from Polzunov Altai State Polytechnical Institute in 1963. Candidate of Sciences in Physics and Mathematics. Since 1965 works at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences; Head of a laboratory. Scientific interest: volcanic acoustics, nature of earthquake predecessors. Author and coauthor of more than 100 papers and one monograph. Rulenko Oleg Petrovich. Born 1946. Graduated with honors from the Department of Physics and Mathematics of the Kamchatka State Pedagogical Institute in 1968. Received candidate’s degree (in Physics and Mathematics) in 1994. Senior Researcher at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences. Scientific interests: atmospheric electricity, interaction of lithosphere and atmosphere, physics of earthquake predecessors. Author of 29 scientific publications.     

Optimal signal interpolation using the values of signal and its derivative

The effect of the bias in a sequence of signal samples and sequence of signal derivative samples on the signal reconstruction accuracy is considered for a bandwidth-limited signal. Vitalii M. Efimov. Born 1933. Graduated from the Moscow Institute of Aviation. Received candidate’s degree in 1964. Leading researcher at the Institute of Automatics and Electrometry, Siberian Division, Russian Academy of Sciences. Scientific interests: signal sampling and quantization and processing and squeezing of digital data. Author of 85 papers. Aleksandr L. Reznik. Born 1948. Graduated from Novosibirsk State University in 1969. Received candidate’s degree in 1981. Head of the laboratory of the Institute of Automatics and Electrometry, Siberian Division, Russian Academy of Sciences. Scientific interests: analytical and numerical methods for solving complex probability problems with computer calculations. Author of 64 papers. Andrei V. Torgov. Born 1959. Graduated from Novosibirsk State University in 1983. Leading software engineer at the Institute of Automatics and Electrometry, Siberian Division, Russian Academy of Sciences. Scientific interests: image processing and digital filtering. Author of 17 papers.     

The use of pattern recognition methods in tasks of biomedical diagnostics and forecasting

Questions of use of pattern recognition methods in medical tasks are discussed. The Recognition program system is presented, which contains a variety of pattern recognition and cluster analysis methods. Some methods based on voting by a system of regularities are discussed in more detail. An example of solved tasks of hysteromyoma relapse forecasting is given. The text was submitted by the authors in English. Yuri Ivanovich Zhuravlev. Born in Voronezh on January 14, 1935. In 1957, graduated from Moscow State University. Doctor of physics and mathematics since 1965, professor since 1967, and an academician of the RAS since 1992. Deputy director of the Dorodnicyn Computing Center of the RAS, chair of the Mathematics Department of the RAS, head of the Chair of Moscow State University, and editor in chief of Pattern Recognition and Image Analysis. Foreign member of the Spanish Royal Academy of Sciences, the National Academy of Sciences of Ukraine, and the European Academy of Sciences. Winner of the Lenin and Lomonosov prizes. Main fields of scientific interest: mathematical logic; control systems theory; the mathematical theory of pattern recognition, image analysis, and forecasting; operation research; and artificial intelligence. Developed such new lines of research as the theory of local optimization algorithms, algorithms of estimate calculation, and the algebraic theory of recognition algorithms. Oleg Valentinovich Senko. Born in Salavat, Bashkortostan, in 1957. In 1981, graduated from the Moscow Institute of Physical Technology; in 1985–1989, took graduate courses, receiving a PhD in 1990. Senior researcher, Dorodnicyn Computing Center, RAS. Doctor of physics and mathematics since 2007. Scientific interests: data mining, mathematical models of pattern recognition, classification and forecasting, and practical applications in medicine and other fields. Vladimir Vasilievich Ryazanov. Graduated from the Moscow Institute of Physical Technology in 1973 and completed graduate courses at the Computing Center of the USSR Academy of Sciences in 1976. Received a PhD in 1977 in computer science and the degree of professor in 1994 (“Applications of Mathematical Methods, Mathematical Modeling, and Computers in Scientific Research”). At the Dorodnicyn Computing Center of the RAS since 1976. Head of the Situations Recognition Sector in the Department of Recognition Problems and Combinatorial Analysis. Author of approximately 150 papers. Scientific interests: data mining; mathematical models of pattern recognition; classification and forecasting; optimization of recognition and classification models; learning; synthesis of optimal collective solutions in the problem of classification; creation of intelligent program systems for data analysis and recognition; and practical applications in technology, medicine, and industry. Anna Victorovna Kuznetsova. Born in Samara (Kuibyshev) in 1961. In 1986 graduated from the Second Moscow Medical Institute. In 1991–1994, took graduate courses, receiving a PhD in biology in 1995. Senior researcher, Emanuel Institute of Biochemical Physics. Scientific interests: pattern recognition, intellectual methods of data analysis, and development of diagnostic and forecasting algorithms in medicine and biological and medical applications in medicine and other fields. Mikhail Afanasievich Botwin. Born in the village of Rashevka of Poltava oblast in Ukraine in 1941. Doctor of medical sciences and Professor of the Sechenov Moscow Medical Academy. Scientific interests: hysteromyoma: pathogenesis, diagnostics, influence on pregnancy, aperiodicities of birth activity, methods of treatment.     

Stochastic filtering of approximate symmetries in dense packings of clusters

Certain questions concerning the arrangement of optimal dense packings of clusters are considered when simple routine long-term procedures are applied instead of a laborious method of direct solution. A unified approach to searching for hidden symmetries in such packings is proposed that represents a certain combination of the generalized Hough transform and the Purzen windows technique in nonparametric density estimation. All symmetries are sought via the Hough transforms adjusted to certain types of adjacent classes on the SO _N group manifold. Exact symmetries and separate solutions are filtered out by using the ergodic properties of the independent sequential choice procedure. Aleksandr Petrovich Vinogradov. Born 1951. Graduated from the Moscow Institute of Physics and Technology in 1974. Received candidates degree in physics and mathematics in the field of mathematical cybernetics. Scientific interests: pattern recognition, image analysis, application of algebraic and geometric methods to the problems of data analysis. Author of 45 papers. Jan Voracek (1962), graduated from Brno University of Technology (BUT) in 1985. Obtained his MS in Technical Cybernetics in 1985, first PhD in Technical Cybernetics in 1992, and second PhD in Manufacturing Technology in 1996, all from BUT. Since 1997 he has been working as a professor of information technology at the Laboratory of Information Processing, Lappeenranta University of Technology, Finland. Author and coauthor of more than 80 publications. Research interests include pattern recognition, image processing, and international education. Yuri I. Zhuravlev. Born 1935. Graduated from the Faculty of Mechanics and Mathematics, Moscow State University, in 1957. Received his PhD (Kandidat Nauk) degree in 1959 and Doctoral (Doktor Nauk) degree in Physics and Mathematics in 1965. Since 1969, with the Computer Center of the Russian Academy of Sciences, Moscow, first as a Laboratory Head and then as a Deputy Director. Professor at Moscow State University. Full member of the Russian Academy of Sciences (since 1992) and of the Academy of Sciences of Spain (since 1993). Scientific interests: mathematical logic; algebra; discrete optimization; pattern recognition; and the use of mathematical and computational methods for solving applied problems of data processing and research automation in industry, medicine, geology, and sociology. Author of more than 170 publications on information technology and applied mathematics. Editor-in-Chief of the journal Pattern Recognition and Image Analysis and a member of the editorial boards of several international and Russian journals on information technology and applied mathematics.     

Prediction of strong earthquakes with the use of the scalar product of matrices with a sliding window

The paper is devoted to the important problem of predicting strong earthquakes in space and time. The solution is based on the hypothesis that the prehistory of strong earthquakes in some region implicates a complex sequence of previous moderate-power earthquakes that is certain to considerable extent and common for various strong earthquakes in this region. The algorithm for finding this commonality is implemented by means of procedures on Boolean matrices with a sliding window and has been approved for the earthquakes in Kamchatka and the Kuril Islands. Sliding examination neatly reveals almost all strong earthquakes that have a long enough prehistory (12 years). It is interesting that such earthquakes are predicted several years prior to the catastrophe with an accuracy of 1 or 2 days in time and 100 km in space. There are no false maxima. “There is a striking possibility of mathematically treating an object without understanding its essence.” A. Einstein Aleksandr Mikhailovich Shurygin. Born 1931. Graduated from Moscow State University, Faculty of Geology (1949–1954) and Faculty of Mechanics and Mathematics (1960–1965). Received candidate’s degree in Geology and Mineralogy (Tectonics) in 1959 and doctoral degree (Information Theory) in 2002. Since 1970, he has had the title of Senior Researcher. He works as a leading researcher at the Faculty of Computational Mathematics and Cybernetics of MSU and as a senior researcher at the Scientific Council of Cybernetics of the RAS. Author of more than 130 scientific publications, including five monographs. Strigunova Mariya S. Born 1976. Graduated from Moscow State University in 1998. Moscow State Academy of Publishing, the Department of Higher Mathematics, assistant. Scientific Council on Cybernetics, Russian Academy of Sciences, applicant for dissertation. Author of nine publications.     

Computational model for recognition of scene objects with an active sensor

In the paper, a computational model for recognition of objects in a scene image is presented. The model is based on the use of an active sensor. The structure of the object model (OM) is described. This structure is a component that stores different representations of the object and puts at user’s disposal an interface whose operations are used in the scene recognition process. Semen Yu. Sergunin. Born 1980. Graduated from the Faculty of Mathematics and Mechanics of Moscow State University in 2002. Finished postgraduate course of the Department of Computational Mathematics of the same faculty. Scientific interests include image recognition. Author of about 20 papers. Mikhail I. Kumskov. Graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in 1978. Received his candidate’s degree (in Physics and Mathematics) in 1981 and doctoral degree in 1997. In 1981–1997 taught at the Faculty of Computational Mathematics and Cybernetics in the special seminar on Computer Graphics and Image Processing of the Department of Automation of Systems of Computational Complexes. Since 1992 works at the laboratory of Mathematical Chemistry of the Zelinsky Institute of Organic Chemistry of the Russian Academy of Sciences. Since 1997 teaches at the Department of Computational Mathematics of the Faculty of Mathematics and Mechanics of Moscow State University. Author of more than 50 papers. Scientific interests include prediction of properties of chemical compounds, optimization of structural object representation for classification problems, and image understanding.     

Fast algorithms for a multidimensional hypercomplex discrete Fourier transform with radix-3 decomposition

Combined algorithms for the multidimensional hypercomplex discrete Fourier transform (HDFT) of a real signal with data representation in the Hamilton-Eisenstein generalized codes are synthesized. The complexity of arithmetic operations in a commutative-associative hypercomplex algebra and its representation in generalized codes are obtained. It is shown that there exist only two essentially different commutative-associative hypercomplex algebras: the direct sums of real or complex algebras. The computational complexity of the algorithm synthesized is estimated. Marat Vyacheslavovich Aliev. Born 1978. Graduated from Adygeya State University in 2000. Received candidate’s degree in physics and mathematics in 2004. Presently he is a senior lecturer at the Department of Applied Mathematics and Information Technologies, Adygeya State University. Scientific interests: image processing, fractals, fast algorithms of discrete transforms, and finite-dimensional algebras. Author of 14 publications, including 7 papers. Member of the Russian Association of Pattern Recognition and Image Analysis. Marina Aleksandrovna Chicheva. Born 1964. Graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1987. Received candidate’s degree in Engineering in 1998. Presently, she is a senior researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: image processing, compression, and fast algorithms of discrete transforms. Author of more than 50 publications, including 18 papers and 1 monograph. Member of the Russian Association of Pattern Recognition and Image Analysis.     

Effectiveness of consideration of deformations in problems of fingerprint identification

In the paper, we consider some refined methods for fingerprint recognition taking into account a distorting factor, namely, elastic deformations arising when a finger contacts with the scanner surface. We show that the consideration of elastic deformations significantly improves the quality of fingerprint recognition. Ushmaev Oleg Stanislavovich. Born 1981. Graduated from the Faculty of Mechanics and Mathematics of Lomonosov Moscow State University in 2002. Received candidate’s degree in 2004. Scientific interests: image processing, pattern recognition, biometrics. Author of 14 publications. Novikov Sergei Olegovich, Senior Researcher at the Institute of Information Problems RAS. Born in 1963. Graduated from the Moscow Institute of Physics and Technology in 1986. In 1990 finished the postgraduate study at the Institute of Information Problems, Russian Academy of Sciences. Candidate of Technical Sciences. Scientific interests: pattern recognition, image processing, mathematical modeling in biology. Author of two patents and more than 20 publications. Member of the International Scientific Societies SPI, IEEE Computer Society, IAPR.     

Estimating directions of optic disk blood vessels in retinal images

The problem of determining directions of blood vessels in the optic disk is considered. The proposed method for estimating the vessel directions is based on analyzing local minima of gray-scale profile of the eye-ground image. Results of tests on real images are presented. Mikhail Anan’in. Born 1984. Graduated from the Samara State Aerospace University in 2007 and is currently a post-graduate student at the same university. From 2006 to present is a junior researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: image processing, image reconstruction, pattern recognition, wavelet analysis, and differential geometry. Authored more than ten papers. Nataliya Il’yasova. Born 1966. Graduated from the Samara State Aerospace University in 1991, where in 1997 she received candidate’s degree (Eng.). Currently a senior s researcher at the Image Processing Systems Institute, Russian Academy of Sciences and a senior lecturer at Samara State Aerospace University. Scientific interests: digital image processing and recognition, pattern recognition, information systems in biomedical applications, computer-aided systems for monitoring eye fundus microvascular morphology, and analysis of cardiac coronary vessels. Author of more than 60 papers in the field of image processing and pattern recognition. Aleksandr Kupriyanov. Born 1978. Graduated from the Samara State Aerospace University in 1991 and in 1997 received candidate’s degree (Eng.) from the same university. Currently has a position of researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: digital image processing and recognition, pattern recognition, information systems in biomedical applications, computer-aided systems for monitoring eye fundus microvascular morphology, analysis of cardiac coronary vessels, evaluation of diagnostic features, and retinal image analysis. Author of more than 30 papers in the field of image processing and pattern recognition.     

Application of probability filter to signal processing problems

A method of signal filtering based on the maximum likelihood principle is presented. This method provides elimination of random distortions caused by noises, conserving local specific features of the signal. The concept of probability filtering ensures processing of both discrete and piecewise-continuous signals. Dmitrii P. Vetrov. Born 1981. Post-graduate student at Moscow State University, to graduate in 2006. Works at the Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: pattern recognition, machine learning, mathematical statistics, expert systems. Nine publications. Dmitrii A. Kropotov. Born 1981. Post-graduate student at Moscow State University, to graduate in 2006. Works at the Dorodnicyn Computing Center, Russian Academy of Sciences. Scientific interests: pattern recognition, machine learning, mathematical statistics, expert systems. Eight publications.