Foam-rubber sample volume measuring

For industrial quality control of foam-rubber material, it is required to measure volume of the sample. A new approach is proposed to measure sample volume by images of sample faces. Faces images are got via flatbed scanner. The faces images are processed and the sample is approximated by hexahedron. Then the sample volume is calculated analytically. Also we proposed an iterative approach based on splitting geometrical model of the sample into several smaller hexahedrons. The test results have shown that results of volume measurements obtained by proposed approach coincide well with ones obtained by the standard method. However, repeatability and reproducibility of measurements is better for proposed algorithm, and it is faster. The article is published in the original. Ilia V. Safonov. Received his MS degree in automatic and electronic engineering from Moscow Engineering Physics Institute/University (MEPhI), Russia in 1994 and his PhD degree in computer science from MEPhI in 1997. Since 1998 he is an associate professor of faculty of Cybernetics of MEPhI while conducting researches in image segmentation, features extraction and pattern recognition problems. Since 2004, Dr. Safonov has joint Image Enhancement Technology Group, Printing Technology Lab, Samsung Research Center, Moscow, Russia where he is engaged in photo, video and document image enhancement projects. Sergei Yu. Yakovlev. Received the MS degree in cybernetics from Moscow Engineering Physics Institute/University (MEPhI), Russia in 2005. He is presently working towards his PhD degree. His current research interests include image processing, pattern recognition and 3D shape reconstruction.     

Hiding a halftone secret image in two camouflaged halftone images

In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images. Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable. The text was submitted by the authors in English. Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding, digital watermarking, and image compression. Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression.     

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.     

Image hiding scheme using modulus function and optimal substitution table

The simple least-significant-bit (LSB) substitution technique is the easiest way to embed secret data in the host image. To avoid image degradation of the simple LSB substitution technique, Wang et al. proposed a method using the substitution table to process image hiding. Later, Thien and Lin employed the modulus function to solve the same problem. In this paper, the proposed scheme combines the modulus function and the optimal substitution table to improve the quality of the stego-image. Experimental results show that our method can achieve better quality of the stego-image than Thien and Lin’s method does. The text was submitted by the authors in English. Chin-Shiang Chan received his BS degree in Computer Science in 1999 from the National Cheng Chi University, Taipei, Taiwan and the MS degree in Computer Science and Information Engineering in 2001 from the National Chung Cheng University, ChiaYi, Taiwan. He is currently a Ph.D. student in Computer Science and Information Engineering at the National Chung Cheng University, Chiayi, Taiwan. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in applied mathematics in 1977 and his MS degree in computer and decision sciences in 1979, both from the National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in computer engineering in 1982 from the National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at the National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a Fellow of IEEE, a Fellow of IEE and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression. Yu-Chen Hu received his Ph.D. degree in Computer Science and Information Engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan in 1999. Dr. Hu is currently an assistant professor in the Department of Computer Science and Information Engineering, Providence University, Sha-Lu, Taiwan. He is a member of the SPIE society and a member of the IEEE society. He is also a member of the Phi Tau Phi Society of the Republic of China. His research interests include image and data compression, information hiding, and image processing.     

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.     

Image enhancement by total variation quasi-solution method

A new method of image restoration based on the quasi-solution method for a compact set of functions with bounded total variation is introduced. Application of this method does not require estimation of the noise level, which is necessary to choose the regularization parameter in the Tikhonov regularization method. The approbation of this method with test images shows its effectiveness for image deringing. The text was submitted by the authors in English. Andrey S. Krylov. Born 1956. Graduated from the Faculty of Computational Mathematics and Cybernetics, Moscow State University (MGU). Received the degree of PhD in 1983. Currently an associate professor and head of the Laboratory of Mathematical Methods of Image Processing at the Faculty of Computational Mathematics and Cybernetics, MGU. His main research interests lie in mathematical methods of multimedia data processing. Vladimir N. Tsibanov. Born 1982. Graduated from the Faculty of Computational Mathematics and Cybernetics, Moscow State University (MGU). He is currently a PhD student at the Faculty of Computational Mathematics and Cybernetics, MGU. His main research interests lie in variational methods in image processing. Alexander M. Denisov. Born 1946. Graduated from the Faculty of Mechanics and Mathematics, Moscow State University (MGU). Received the degrees of PhD in 1972 and doctor of science in 1987. Currently a professor and head of the Chair of Mathematical Physics of the Faculty of Computational Mathematics and Cybernetics, MGU. His main research interests lie in inverse and ill-posed problems in mathematical physics.     

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.     

OCR applied to an electronic voting system

This paper describes a new electronic secure voting system based on automatic paper ballot reading. It presents how the system is organized, it also describes our OCR system and how it is implemented to read paper ballots, and it ends showing some experimental results. The first step of the OCR system consists in extracting from each character several simple features, which help us to perform distortion processing. These simple features are used to define a key which possibly allows us to identify the character. If this is not the matter and there are several candidates for the obtained key, we need to extract more complex features. This second process is based on the use of floating masks, which are specific for each feature, and on the following of its trajectory through the character stroke. The text was submitted by the authors in English. J.K. Espinosa received MS and PhD degrees in Electrical Engineering from the University of the Basque Country, Spain, in 1989 and 2002, respectively. Since 1989, he has been an assistant professor in Telematic Engineering at the Electronics and Telecommunications Department of the University of the Basque Country. He teaches courses in computer programming, data communication, and computer networks and services. His research interests include digital image processing software, optical character recognition, electronic voting systems, video transmission over the Internet, and telematic applications. I. Goirizelaia is professor in the Electronics and Telecommunication Department at the School of Engineering, University of the Basque Country. He received his electrical engineering degree in 1981 and his PhD in electrical engineering in 1987, both from the University of the Basque Country. He worked for Stanford Research Institute (1983–1985) as an international fellow and for LABEIN research laboratory (1986), and in 1987 he started his own company dedicated to industrial applications of image processing techniques. He was vice president for university enterprise relations of the University of the Basque Country from 1998 to 2000. He was a visiting scientist at the MIT Media Lab for six months in 2004. His research interest is the development of advanced information technology, focusing on teleeducation, web based learning environments, and electronic voting technology. He is also interested in security schemes based on image processing algorithms applied to watermarking of digital images. Currently, he is vice president of the University of the Basque Country. J.J. Igarza received his MS degree in 1986 from the Physics Faculty and his MS degree in software engineering in 1997 from the Engineering School of the University of the Basque Country. From 1986 to 1996, he worked as a researcher for a machine vision company, and since 1997, he has been a lecturer at the Engineering School of Bilbao. His research interests include multimodal biometric databases, on-line and offline signature verification, and human-machine interactions.     

ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical 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.     

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.     

Clustering of signal components within most likely ECG episodes to analyze the ECG-waves

A computational cardiology based approach to find the number of ECG-waves responsible for generation and presentation of an ECG episode is proposed. The methodology adopted here is based on the comparison and study of diagnostic efficiency calculated from 12 lead ECG data. The diagnostic-efficiency is measured by using NN classifier whereas clustered points of most likely ECG-episode constitute the diagnostic feature. The result is also cross-validated using episodes transformed by existing signal transform technique, “Discrete Fourier Transform” and another transformation. “Velocity-Wave number Transform.” The result indicates the possible number of episode-clusters giving maximum diagnostic efficiency and also gives a new insight on ECG waves. The article is published in the original. Tapobrata Lahiri. Born in 1966. Graduated from Kalyani University, India in 1989. Received his PhD degree in Bio-physics in 1998. Assistant Professor at Indian Institute of Information Technology, Allahabad, India. Author of more than 25 publications. Scientific interests: Pattern Recognition. Systems Modelling and Simulation, Operation Research, Fractal and Chaos Analysis, Medical Informatics. Bioinformatics. Subrata Sarkar. Born in 1983. Received BE (Electronics and Communication) degree from Burdwan University, India in 2005. Doing his PhD in Engineering from Jadavpur University, Kolkata. Junior Research Fellow in DST, India sponsored ILTP Indo-Russian project. Scientific interests: Medical signal processing, Medical image processing. Aleksei Morozov. Born in 1968. Graduated from Bauman State Technical University, Moscow, in 1991. Received his PhD (Kandidat Nauk) degree in Physics and Mathematics in 1998. Senior Researcher at the Institute of Radio Engineering and Electronics of the Russian Academy of Sciences. Author of more than 60 publications. Scientific interests: biomedical signals analysis, logic programming, Internet agents. Sudip Sanyal. Born in 1957. Graduated from Banaras Hindu University, Varanasi, India in 1980. Received his PhD degree in Theoretical Nuclear Physics in 1986. Associate Professor at Indian Institute of Information technology, Allahabad, India. Author of more than 90 publications. Scientific interests: Pattern recognition, Information retrieval. Yurii Obukhov. Born in 1960. Graduated from the Moscow Institute of Physics and Technology in 1974. Obtained his Doctoral (Doctor Nauk) degree in Physics and Mathematics in 1992. Head of the Laboratory of the Institute of Radio Engineering and Electronics of the Russian Academy of Sciences. Current research interests: data visualizing, image analysis and processing, biomedical information science, and information systems. Author of more than 70 publications. Member of Editors Board of Biomedical Radioelectronics journal     

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selection was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitoring of machines. The text was submitted by the authors in English. Xianguo Hu (born 1963), PhD, is a professor at the School of Mechanical and Automotive Engineering at the Hefei University of Technology, China. He received his BS and MS in Powder Metallurgy Material and Mechanics (Tribology) from the Hefei University of Technology in 1985 and 1988, respectively. His PhD degree was awarded at Szent Istvan University, Hungary, in 2002. As a visiting scientist, he conducted research at the Technical University of Budapest, Hungary, and the Technical University of Berlin, Germany, from 1994 to 1997. His research areas include wear debris analysis, optimal tribological design, friction and wear mechanisms, etc. He is the author or coauthor of more than 100 published technical papers. Peng Huang (born 1981) is an MS student at the School of Mechanical and Automotive Engineering of Hefei University of Technology, China. His main focus is on wear debris analysis. Shousen Zheng (born 1963) is an associate professor at the School of Engineering, SunYat-Sen University, China. He received his BS, MS, and PhD in Mechanical Engineering from Hefei University of Technology in 1985, 1988, and 2001, respectively. From 1988 to 2004, he was employed at the Department of Mechanical Engineering at the Hefei University of Technology. In 2005, he moved to the current university. His research interests include computer language, auto CAD/CAM, wear debris analysis, etc. He is the author or coauthor of more than 40 published technical papers.     

Anisotropic Curvature Motion for Structure Enhancing Smoothing of 3D MR Angiography Data

We propose a novel concept of shape prior for the processing of tubular structures in 3D images. It is based on the notion of an anisotropic area energy and the corresponding geometric gradient flow. The anisotropic area functional incorporates a locally adapted template as a shape prior for tubular vessel structures consisting of elongated, ellipsoidal shape models. The gradient flow for this functional leads to an anisotropic curvature motion model, where the evolution is driven locally in direction of the considered template. The problem is formulated in a level set framework, and a stable and robust method for the identification of the local prior is presented. The resulting algorithm is able to smooth the vessels, pushing solution toward elongated cylinders with round cross sections, while bridging gaps in the underlying raw data. The implementation includes a finite-element scheme for numerical accuracy and a narrow band strategy for computational efficiency. Oliver Nemitz received his Diploma in mathematics from the university of Duisburg, Germany in 2003. Then he started to work on his Ph.D. thesis in Duisburg. Since 2005 he is continuing the work on his Ph.D. project at the Institute for Numerical Simulation at Bonn University. His Ph.D. subject is fast algorithms for image manipulation in 3d, using PDE’s, variational methods, and level set methods. Martin Rumpf received his Ph.D. in mathematics from Bonn University in 1992. He held a postdoctoral research position at Freiburg University. Between 1996 and 2001, he was an associate professor at Bonn University and from 2001 until 2004 full professor at Duisburg University. Since 2004 he is now full professor for numerical mathematics and scientific computing at Bonn University. His research interests are in numerical methods for nonlinear partial differential equations, geometric evolution problems, calculus of variations, adaptive finite element methods, image and surface processing. Tolga Tasdizen received his B.S. degree in Electrical Engineering from Bogazici University, Istanbul in 1995. He received the M.S. and Ph.D. degrees in Engineering from Brown University in 1997 and 2001. From 2001 to 2004 he was a postdoctoral research associate with the Scientific Computing and Imaging Institute at the University of Utah. Since 2004 he has been with the School of Computing at the University of Utah as a research assistant professor. He also holds an adjunct assistant professor position with the Department of Neurology and the Center for Alzheimer’s Care, Imaging and Research, and a research scientist position with the Scientific Computing and Imaging Institute at the University of Utah. Ross Whitaker received his B.S. degree in Electrical Engineering and Computer Science from Princeton University in 1986, earning Summa Cum Laude. From 1986 to 1988 he worked for the Boston Consulting Group, entering the University of North Carolina at Chapel Hill in 1989. At UNC he received the Alumni Scholarship Award, and completed his Ph.D. in Computer Science in 1994. From 1994–1996 he worked at the European Computer-Industry Research Centre in Munich Germany as a research scientist in the User Interaction and Visualization Group. From 1996–2000 he was an Assistant Professor in the Department of Electrical Engineering at the University of Tennessee. He is now an Associate Professor at the University of Utah in the College of Computing and the Scientific Computing and Imaging Institute.     

Enhanced motion parameters estimation for an active vision system

In this paper, we present an enhanced approach for estimating 3D motion parameters from 2D motion vector fields. The proposed method achieves valuable reduction in computational time and shows high robustness against noise in the input data. The output of the algorithm is part in a multiobject segmentation approach implemented in an active vision system. Hence, the improvement in the motion parameters estimation process leads to speed-up in the overall segmentation process. The text was submitted by the authors in English. Mohamed Shafik obtained his B.Sc. in mechanical engineering at the University of Banha. In 2004 he earned an Information Technology Diploma in Mechatronics from the Information Technology Institute (ITI). In 2006 he obtained his M.Eng. in applied mechatronics at the University of Paderborn. Since 2006, he is a PhD student and a scientific assistant in the GET Lab. His research interests focus on robotic vision, neural networks, and mechatronic systems. Baerbel Mertsching studied electrical engineering and obtained her PhD at the University of Paderborn. Between 1994 and 2003, she was professor of computer science at the University of Hamburg. In 2003 she returned to the University of Paderborn where she is now professor of electrical engineering and director of the GET Lab. Her research interests focus on cognitive systems engineering, especially active vision systems, and microelectronics for image and speech processing. She has been a member of a variety of scientific councils and editorial boards and is author of more than 120 scientific publications.     

Using Catastrophe Theory to Derive Trees from Images

In order to investigate the deep structure of Gaussian scale space images, one needs to understand the behaviour of critical points under the influence of blurring. We show how the mathematical framework of catastrophe theory can be used to describe the different types of annihilations and the creation of pairs of critical points and how this knowledge can be exploited in a scale space hierarchy tree for the purpose of a topology based segmentation. A key role is played by scale space saddles and iso-intensity manifolds through them. We discuss the role of non-generic catastrophes and their influence on the tree and the segmentation. Furthermore it is discussed, based on the structure of iso-intensity manifolds, why creations of pairs of critical points don’t influence the tree. We clarify the theory with an artificial image and a simulated MR image.Arjan Kuijper received his M.Sc. degree in applied mathematics in 1995 with a thesis on the comparision of two image restoration techniques, from the University of Twente, The Netherlands. During the period 1996–1997 he worked at ELTRA Parkeergroep, Ede, The Netherlands. In the period 1997-2002 he has been a Ph.D. student and associate researcher at the Institute of Information and Computing Sciences of Utrecht University. In 2002 he received his Ph.D. degree with a thesis on “Deep Structure of Gaussian Scale Space Images” and worked as postdoc at Utrecht University on the project “Co-registration of 3D Images” on a grant of the Netherlands Ministry of Economic Affairs within the framework of the Innovation Oriented Research Programme. Since Januari 1st 2003 he has been working as an assistant research professor at the IT University of Copenhagen in Denmark funded by the IST Programme “Deep Structure, Singularities, and Computer Vision (DSSCV)” of the European Union. His interest subtends all mathematical aspects of image analysis, notably multiscale representations (scale spaces), catastrophe and singularity theory, medial axes and symmetry sets, and applications to medical imaging.Luc M.J. Florack received his M.Sc. degree in theoretical physics invv 1989, and his Ph.D. degree in 1993 with a thesis on image structure, both from Utrecht University, The Netherlands. During the period 1994–1995 he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and INESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 until June 2001 he was an assistant research professor at Utrecht University at the Department of Mathematics and Computer Science. Since June 1st 2001 he is with Eindhoven University of Technology, Department of Biomedical Engineering, currenlty employed as an associate professor. His interest subtends all structural aspects of signals, images and movies, notably multiscale representations, and their applications to imaging and vision.     

Achieving dynamic, multi-commander, multi-mission planning and execution

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed. Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI²AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics. Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996. Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.     

A new texture representation approach based on local feature saliency

Although it has been studied in some depth, texture characterization is still a challenging issue for real-life applications. In this study, we propose a multiresolution salient-point-based approach in the wavelet domain. This incorporates a two-phase feature extraction scheme. In the first phase, each wavelet subband (LH, HL, or HH) is used to compute local features by using multidisciplined (statistical, geometrical, or fractal) existing texture measures. These features are converted into binary images, called salient point images (SPIs), via threshold operation. This operation is the key step in our approach because it provides an opportunity for better segmentation and combination of multiple features. In the final phase, we propose a set of new texture features, namely, salient-point density (SPD), non-salient-point density (NSPD), salient-point residual (SPR), saliency and non-saliency product (SNP), and salient-point distribution non-uniformity (SPDN). These features characterize various aspects of image texture such as fineness/coarseness, primitive distribution, internal structures, etc. These features are then applied to the well-known K-means algorithm for unsupervised segmentation of texture images. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potential of the proposed features compared to the wavelet energy (WE) and local extrema density feature (LED). The text was submitted by the authors in English. Md. Khayrul Bashar was born in Chittagong, Bangladesh in 1969. He received his B.E. (1993), M.Tech. (1998), and PhD (2004) degrees from Bangladesh University of Engineering and Technology (BUET), Indian Institute of Technology (IIT) Bombay, and Nagoya University, respectively. He was a research engineer from 1995 to 1999 at Bangladesh Space Research and Remote Sensing Organization (SPARRSO) and assistant professor from 1999 to 2000 at the department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (CUET), Bangladesh. Since 2004, he has been a research fellow in the department of Information Engineering, Nagoya University, Japan. Dr. Bashar is a member of IEEE, IEICE, BCS, and IEB. His research interest includes developing algorithms for image understanding, content-based image retrieval and web-application design, analysis and testing. Noboru Ohnishi was born in Aichi, Japan in 1951. He received his B.E., M.E., and PhD degrees in Electrical Engineering from Nagoya University in 1973, 1975, and 1984, respectively. From 1975–1986, he worked as a researcher in the Rehabilitation Engineering centre under the Ministry of Labor, Japan. In 1986, he joined as an Assistant Professor in the dept. of Electrical Engineering of Nagoya University. Currently, he is a professor of the dept. of Information Engineering at the same university. During his long professional life, he has also served as a visiting researcher (1992–1993) in the laboratory of artificial intelligence at Michigan University, and team leader (1993–2001) at the Bio-mimetic Control Research Center, RIKEN, Nagoya, Japan. He also holds many respectable positions at various professional bodies in Japan and he has published many research papers (more than 140) in various international journals. For his technical creativity and ingenuity, he was awarded SICE society prizes in 1996 and 1999. His research interest includes brain analysis, modeling, and brain support, computer vision, and audition. He is a member of IEEE, IPSJ, IEICE, IEEJ, IIITE, JNNS, SICE and RSJ. Kiyoshi Agusa received his PhD degree in computer science from Kyoto University in 1982. Currently, he is a professor of the department of Information Systems, Graduate School of Information Science, Nagoya University. His research area includes software engineering, program repository, and software reuse. Since 2003, he has been working as a team leader of a university-industry collaboration project entitled “e-Society,” which is a part of the “e-Japan” project, and doing research on reliability issues for web-based applications. He is a member of IPSJ, ISSST, IEICE, ACM and IEEE.     

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.