Adaptive correlation filters for pattern recognition

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984 and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. He is now a titular researcher at the Centro de Investigatión Cientifica y de Educatión Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing and pattern recognition. Mikhail Mozerov received his MS degree in Physics from Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He is with the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, and digital holography. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. He received his candidate’s degree in 1953 and doctoral degree in Information Theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of the IEEE and Popov Radio Society.     

Adaptive nonlinear composite filters for pattern recognition

Adaptive composite nonlinear filters for reliable illumination-invariant pattern recognition are proposed. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given value of discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in test nonuniform illuminated scenes are discussed and compared with those of linear composite correlation filters in terms of recognition performance. The text was submitted by the authors in English. Saul Martínez Diaz. Received his MSc degree in Computer Science from Instituto Tecnologico de La Paz, Mexico in 2005. He is currently a PhD student at Department of Computer Science, CICESE, Mexico. His research interests include nonlinear image processing and pattern recognition. Vitaly Kober. Obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, his PhD degree in 1992, and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at CICESE, Mexico. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich. Graduated from the Moscow Electrotechnical Institute of Telecommunications. Received Candidate’s degree in 1953 and Doctor’s degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

A robust dynamic programming algorithm for motion detection and estimation

A new robust algorithm for motion detection and precise evaluation of the motion vectors of moving objects in a sequence of images is presented. It is well known that the accuracy of estimating motion vectors estimation is limited by smoothness constraints and mutual occlusions of motion segments. The proposed method is a fusion of block-matching motion estimation and global optimization technique. It is robust to motion discontinuity and moving objects occlusions. To avoid some contradictions between global optimization techniques and piece-wise smooth values of sought motion vectors, a hidden segmentation model is utilized. Computer simulation and experimental results demonstrate an excellent performance of the method in terms of dynamic motion analysis. This article was translated by the authors. Mikhail Mozerov received his MS degree in Physics from the Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He works at the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, digital holography. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigación Científica y de Educación Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present, he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Algorithms for impulse noise removal from corrupted color images

Two effective algorithms for the removal of impulse noise from color images are proposed. The algorithms consist of two steps. The first algorithm detects outliers with the help of spatial relations between the components of a color image. Next, the detected noise pixels are replaced with the output of a vector median filter over a local spatially connected area excluding the outliers, while noise-free pixels are left unaltered. The second algorithm transforms a color image to the YCbCr color space that perfectly separates the intensity and color information. Then outliers are detected using spatial relations between transformed image components. The detected noise pixels are replaced with the output of a modified vector median filter over a spatially connected area. Simulation results in test color images show a superior performance of the proposed algorithms compared with the conventional vector median filter. The comparisons are made using the mean square error, the mean absolute error, and a subjective human visual error criterion. This article was translated by the authors. Vitaly Kober obtained his MS degree in applied mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in image processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centre de Investigación Cientifica y de Educacion Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Mikhail Mozerov received his MS degree in physics from Moscow State University in 1982 and his PhD degree in image processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He works at the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, digital holography. Alvarez-Borrego Josué obtained his MS degree in optics from the Centro de Investigatión Científica y de Educatión Superior de Ensenada (Cicese), México, in 1983, and his PhD degree in optics from the Cicese in 1993. He is a titular researcher at the Cicese. His research interests include image processing and pattern recognition applied to study marine surfaces, statistical and biogenic particles. He has more than 25 scientific papers. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present, he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Image restoration with sliding sinusoidal transforms

A space-variant restoration technique with sliding sinusoidal transforms is presented. Minimum mean-square error estimators in the domains of sliding discrete sine and cosine transforms for image restoration are derived. To achieve image processing in real time, fast recursive algorithms for computing the sliding sinusoidal transforms are utilized. Computer simulation results using a real image are provided and compared with that of common restoration techniques. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, his PhD degree in 1992, and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at CICESE, México. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received Candidate’s degree in 1953 and Doctor’s degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Fast running algorithm for discrete <Emphasis Type="Italic">W</Emphasis> transform

A fast algorithm for computing the running type-II discrete W transform (DWT-II) is proposed. The algorithm is based on a recursive relationship between three subsequent local DWT-II spectra. The computational complexity of the algorithm is compared with that of known fast and running DWT-II algorithms. Fast inverse algorithms for signal processing in the domain of the running DWT-II are also proposed. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Partial evaluation technique for distributed image processing

In this paper, a partial evaluation technique to reduce communication costs of distributed image processing is presented. It combines application of incomplete structures and partial evaluation together with classical program optimization such as constant-propagation, loop unrolling and dead-code elimination. Through a detailed performance analysis, we establish conditions under which the technique is beneficial. Andrei Tchernykh received his Ph.D. degree in computer science from the Institute of Precise Mechanics and Computer Technology of the Russian Academy of Sciences (RAS), Russia in 1986. From 1975 to 1995 he was with the Institute of Precise Mechanics and Computer Technology of the RAS, Scientific Computer Center of the RAS, and at Institute for High Performance Computer Systems of the RAS, Moscow, Russia. Since 1995 he has been working at Computer Science Department at the CICESE Research Center, Ensenada, Baja California, Mexico. His main interests include cluster and Grid computing, incomplete information processing, and on-line scheduling. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctoral degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigation Cientifica y de Educatión Superior de Ensenada (Cicese), México. His research interests include signal and image processing, pattern recognition. Alfredo Cristóbal-Salas received his Ph.D. degree in computer science from the Computer Science Department at the CICESE Research Center, Ensenada, Baja California, México. Now he is a researcher at School of Chemistry Sciences and Engineering, University of Baja California, Tijuana, B.C. Mexico His main interests include cluster and Grid computing, incomplete information processing, and online scheduling. Iosif A. Ovseevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

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.     

Stabilization of the compressed data formation rate in hierarchical image compression

A new method is proposed for stabilizing the rate of compressed data formation in the case of hierarchical image compression. The method is based on using various values of the control parameter (maximum error) for various scale levels of image representation and for error correction at the last level depending on the received compressed information content. Gashnikov Mikhail Valer’evich (b. 1975) graduated from the Samara State Aerospace University (SSAU) in 1998. In 2004, he defended his Ph.D. thesis in engineering. At present, Gashnikov is an associate lecturer at the Department of Geoinformatics (SSAU). The scope of his scientific interests includes image processing, compression, and statistical coding. He has 30 publications, including 12 papers and a monograph (with co-authors). He is a member of the Russian Association for Image Recognition and Analysis. Glumov Nikolai Ivanovich (b. 1962) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1985. In 1994, he defended his Ph.D. thesis in engineering. At present, Glumov is a senior scientist at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include image processing and recognition, image compression, and simulation of digital image formation systems. He has more than 60 publications, including 20 articles and a monograph (with co-authors). He is a member of the Russian Association of Image Recognition and Analysis. Sergeyev Vladislav Viktorovich (b. 1951) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1974. In 1993, he defended his D.Sc. (eng.) thesis. At present, V.V. Sergeyev heads the Laboratory of Mathematical Methods of Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include digital signal processing, image analysis, image recognition, and geoinformatics. He has more than 150 publications, including 40 articles and two monographs (with co-authors). V.V. Sergeyev is the chairman of the Povolzh’e Division of the Russian Association for Pattern Recognition and Image Analysis and a Corresponding Member of the Russian Academy of Ecology and the Academy of Engineering Sciences of the Russian Federation, a member of SPIE (International Society for Optical Engineering), and a winner of the Samara Regional Award in Science and Engineering.     

Family of models for describing one class of metallographic images

Models for images syntax are developed, tried, and tested in describing the syntax of microstructural metallographic images of wrought aluminum alloys. Gennadii Mikhailovich Tsibul’skii was born in 1947 and graduated from Krasnoyarsk Polytechnic Institute in 1973. Since 1975, he has been involved in the analysis of digital images. In 1978, he completed his postgraduate course at the Lenin Leningrad Electronic Technical Institute. He received his candidate’s degree in 1987 and a doctoral degree in engineering in 2006. He was appointed a professor in 2007. In 1996, he founded the Artificial Intelligence Systems Department and has worked there as a chairman since then. His scientific interests include the multiagent approach to images analysis, and he is the author of more than 70 publications (including one book published by the Siberian Branch of the Russian Academy of Sciences). At present, Gennadii Tsibul’skii is the director of the Space and Information Technologies Institute at Krasnoyarsk Siberian Federal University. Yurii Anatol’evich Maglinest was born 1965 and graduated from Krasnoyarsk Polytechnic Institute in 1973; he then pursued postgraduate studies there. He received his candidate’s degree in engineering in 1996 in the analysis of metallographic images. He is an associate professor at the State Commission for Academic Degrees and Titles of the Russian Federation. At present, he is a chair of the Scientific University Laboratory of Flexible Software Systems at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University. His scientific interests include aerospace information storage, processing and analysis, and flexible software systems. Dmitrii Al’bertovich Perfil’ev was born in 1968 and graduated from Krasnoyarsk Polytechnic Institute in 1992. Since 2000, he has been specializing in problems in digital images analysis and, in particular, in describing microstructural pictures of aluminum alloys. He received his candidate’s degree in engineering in 2007 and is the author of 8 publications related to the problem in question. At present, he is a researcher and a lecturer at the Artificial Intelligence Systems Department at Krasnoyarsk Siberian Federal University.