Reverse engineering the way humans rank textures

We argue that in order to understand which features are used by humans to group textures, one must start by computing thousands of features of diverse nature, and select from those features those that allow the reproduction of perceptual groups or perceptual ranking created by humans. We use the Trace transform to produce such features here. We compare these features with those produced from the co-occurrence matrix and its variations. We show that when one is not interested in reproducing human behaviour, the elements of the co-occurrence matrix used as features perform best in terms of texture classification accuracy. However, these features cannot be “trained” or “selected” to imitate human ranking, while the features produced from the Trace transform can. We attribute this to the diverse nature of the features computed from the Trace transform.

Variational Image Binarization and its Multi-Scale Realizations

A variational approach for image binarization is discussed in this paper. The approach is based on the interpolation of surface. This interpolation is computed using edge points as interpolating points and minimizing an energy functional which interpolates a smooth threshold surface. A globally convergent Sequential Relaxation Algorithm (SRA) is proposed for solving the optimization problem. Moreover, our algorithm is also formulated in a multi-scale framework. The performance of our method is demonstrated on a variety of real and synthetic images and compared with traditional techniques. Examples show that our method gives promising results.This research is partially supported by HKBU Faculty Research Grant FRG/02-03/II-04 and NSF of China Grant. C.S. Tong received a BA degree in Mathematics and a Ph.D. degree (on Mathematical Modelling of Intermolecular Forces) both from Cambridge University. After graduation, he joined the Signal and Image Processing division of GEC-Marconis Hirst Research Centre as a Research Scientist, working on image restoration and fractal image compression. He then moved to the Department of Mathematics at Hong Kong Baptist University in 1992, becoming Associate Professor since 2002.He is a member of the IEEE, a Fellow of the Institute of Mathematics and Its Application, and a Chartered Mathematician. His current research interests include image processing, fractal image compression, and neural networks. Yongping Zhang received the M. S. degree from Department of Mathematics at Shaanxi Normal University, Xian, China, in 1988 and received the Ph.D. degree from The Institute of Artificial Intelligence and Robotics at Xian Jiaotong University, Xian, China, in 1998.In 1988 he joined Department of Mathematics at Shaanxi Normal University, where he became Associate Professor in July 1987. He held postdoctoral position at Northwestern Polytechnic University during the 1999–2000 academic years. Currently he is a research associate in the Bioengineering Institute at the University of Auckland, New Zealand. His research interests are in Computer Vision and Pattern Recognition, and include Wavelets, Neural Networks, PDE methods and variational methods for image processing. Nanning Zheng received the M.S. degree from Xian Jiaotong University, Xian, China, in 1981 and the Ph.D. degree from Keio University, Japan, in 1985. He is an academician of Chinese Engineer Academy, and currently a Professor at Xian Jiaotong University. His research interest includes Signal Processing, Machine Vision and Image Processing, Pattern Recognition and Virtual Reality.This revised version was published online in June 2005 with correction to CoverDate     

Geo-located image categorization and location recognition

Image categorization is undoubtedly one of the most recent and challenging problems faced in Computer Vision. The scientific literature is plenty of methods more or less efficient and dedicated to a specific class of images; further, commercial systems are also going to be advertised in the market. Nowadays, additional data can also be attached to the images, enriching its semantic interpretation beyond the pure appearance. This is the case of geo-location data that contain information about the geographical place where an image has been acquired. This data allow, if not require, a different management of the images, for instance, to the purpose of easy retrieval from a repository, or of identifying the geographical place of an unknown picture, given a geo-referenced image repository. This paper constitutes a first step in this sense, presenting a method for geo-referenced image categorization, and for the recognition of the geographical location of an image without such information available. The solutions presented are based on robust pattern recognition techniques, such as the probabilistic Latent Semantic Analysis, the Mean Shift clustering and the Support Vector Machines. Experiments have been carried out on a couple of geographical image databases: results are actually very promising, opening new interesting challenges and applications in this research field. The article is published in the original. Marco Cristani received the Laurea degree in 2002 and the Ph.D. degree in 2006, both in Computer Science from the University of Verona, Verona, Italy. He was a visiting Ph.D. student at the Computer Vision Lab, Institute for Robotics and Intelligent Systems School of Engineering (IRIS), University of Southern California, Los Angeles, in 2004–2005. He is now an Assistant Professor with the Department of Computer Science, University of Verona, working with the Vision, Image Processing and Sounds (VIPS) Lab. His main research interests include statistical pattern recognition, generative modeling via graphical models, and non-parametric data fusion techniques, with applications on surveillance, segmentation and image and video retrieval. He is the author of several papers in the above subjects and a reviewer for several international conferences and journals. Alessandro Perina received the BD and MS degrees in Information Technologies and Intelligent and Multimedia Systems from the University of Verona, Verona, Italy, in 2004 and 2006, respectively. He is currently a Ph.D. candidate in the Computer Science Department at the University of Verona. His research interests include computer vision, machine learning and pattern recognition. He is a student member of the IEEE. Umberto Castellani is Ricercatore (i.e., Research Assistant) of Department of Computer Science at University of Verona. He received his Dottorato di Ricerca (Ph.D.) in Computer Science from the University of Verona in 2003 working on 3D data modelling and reconstruction. During his Ph.D., he had been Visiting Research Fellow at the Machine Vision Unit of the Edinburgh University, in 2001. In 2007 he has been an Invited Professor for two months at the LASMEA laboratory in Clermont-Ferrand, France. In 2008, he has been Visiting Researcher for two months at the PRIP laboratory of the Michigan State University (USA). His main research interests concern the processing of 3D data coming from different acquisition systems such as 3D models from 3D scanners, acoustic images for the vision in underwater environment, and MRI scans for biomedical applications. The addressed methodologies are focused on the intersections among Machine Learning, Computer Vision and Computer Graphics. Vittorio Murino received the Laurea degree in electronic engineering in 1989 and the Ph.D. degree in electronic engineering and computer science in 1993, both from the University of Genoa, Genoa, Italy. He is a Full Professor with the Department of Computer Science, University of Verona. From 1993 to 1995, he was a Postdoctoral Fellow in the Signal Processing and Understanding Group, Department of Biophysical and electronic Engineering, University of Genoa, where he supervised of research activities on image processing for object recognition and pattern classification in underwater environments. From 1995 to 1998, he was an Assistant Professor of the Department of Mathematics and Computer Science, University of Udine, Udine, Italy. Since 1998, he has been with the University of Verona, where he founded and is responsible for the Vision, Image processing, and Sound (VIPS) Laboratory. He is scientifically responsible for several national and European projects and is an Evaluator for the European Commission of research project proposals related to different scientific programmes and frameworks. His main research interests include computer vision and pattern recognition, probabilistic techniques for image and video processing, and methods for integrating graphics and vision. He is author or co-author of more than 150 papers published in refereed journals and international conferences. Dr. Murino is a referee for several international journals, a member of the technical committees for several conferences (ECCV, ICPR, ICIP), and a member of the editorial board of Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Pattern Analysis and Applications and Electronic Letters on Computer Vision and Image Analysis (ELCVIA). He was the promotor and Guest Editor off our special issues of Pattern Recognition and is a Fellow of the IAPR.     

An Integrated Framework for Semantic Annotation and Adaptation

Tools for the interpretation of significant events from video and video clip adaptation can effectively support automatic extraction and distribution of relevant content from video streams. In fact, adaptation can adjust meaningful content, previously detected and extracted, to the user/client capabilities and requirements. The integration of these two functions is increasingly important, due to the growing demand of multimedia data from remote clients with limited resources (PDAs, HCCs, Smart phones). In this paper we propose an unified framework for event-based and object-based semantic extraction from video and semantic on-line adaptation. Two cases of application, highlight detection and recognition from soccer videos and people behavior detection in domotic^* applications, are analyzed and discussed.Domotics is a neologism coming from the Latin word domus (home) and informatics.Marco Bertini has a research grant and carries out his research activity at the Department of Systems and Informatics at the University of Florence, Italy. He received a M.S. in electronic engineering from the University of Florence in 1999, and Ph.D. in 2004. His main research interest is content-based indexing and retrieval of videos. He is author of more than 25 papers in international conference proceedings and journals, and is a reviewer for international journals on multimedia and pattern recognition.Rita Cucchiara (Laurea Ingegneria Elettronica, 1989; Ph.D. in Computer Engineering, University of Bologna, Italy 1993). She is currently Full Professor in Computer Engineering at the University of Modena and Reggio Emilia (Italy). She was formerly Assistant Professor (‘93–‘98) at the University of Ferrara, Italy and Associate Professor (‘98–‘04) at the University of Modena and Reggio Emilia, Italy. She is currently in the Faculty staff of Computer Engenering where has in charges the courses of Computer Architectures and Computer Vision.Her current interests include pattern recognition, video analysis and computer vision for video surveillance, domotics, medical imaging, and computer architecture for managing image and multimedia data.Rita Cucchiara is author and co-author of more than 100 papers in international journals, and conference proceedings. She currently serves as reviewer for many international journals in computer vision and computer architecture (e.g. IEEE Trans. on PAMI, IEEE Trans. on Circuit and Systems, Trans. on SMC, Trans. on Vehicular Technology, Trans. on Medical Imaging, Image and Vision Computing, Journal of System architecture, IEEE Concurrency). She participated at scientific committees of the outstanding international conferences in computer vision and multimedia (CVPR, ICME, ICPR, ...) and symposia and organized special tracks in computer architecture for vision and image processing for traffic control. She is in the editorial board of Multimedia Tools and Applications journal. She is member of GIRPR (Italian chapter of Int. Assoc. of Pattern Recognition), AixIA (Ital. Assoc. Of Artificial Intelligence), ACM and IEEE Computer Society.Alberto Del Bimbo is Full Professor of Computer Engineering at the Università di Firenze, Italy. Since 1998 he is the Director of the Master in Multimedia of the Università di Firenze. At the present time, he is Deputy Rector of the Università di Firenze, in charge of Research and Innovation Transfer. His scientific interests are Pattern Recognition, Image Databases, Multimedia and Human Computer Interaction. Prof. Del Bimbo is the author of over 170 publications in the most distinguished international journals and conference proceedings. He is the author of the “Visual Information Retrieval” monography on content-based retrieval from image and video databases edited by Morgan Kaufman. He is Member of IEEE (Institute of Electrical and Electronic Engineers) and Fellow of IAPR (International Association for Pattern Recognition). He is presently Associate Editor of Pattern Recognition, Journal of Visual Languages and Computing, Multimedia Tools and Applications Journal, Pattern Analysis and Applications, IEEE Transactions on Multimedia, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He was the Guest Editor of several special issues on Image databases in highly respected journals.Andrea Prati (Laurea in Computer Engineering, 1998; PhD in Computer Engineering, University of Modena and Reggio Emilia, 2002). He is currently an assistant professor at the University of Modena and Reggio Emilia (Italy), Faculty of Engineering, Dipartimento di Scienze e Metodi dell’Ingegneria, Reggio Emilia. During last year of his PhD studies, he has spent six months as visiting scholar at the Computer Vision and Robotics Research (CVRR) lab at University of California, San Diego (UCSD), USA, working on a research project for traffic monitoring and management through computer vision. His research interests are mainly on motion detection and analysis, shadow removal techniques, video transcoding and analysis, computer architecture for multimedia and high performance video servers, video-surveillance and domotics. He is author of more than 60 papers in international and national conference proceedings and leading journals and he serves as reviewer for many international journals in computer vision and computer architecture. He is a member of IEEE, ACM and IAPR.     

Universal method for controlling maximum error in image compression

A method for controlling the maximum restoration error in image compression is proposed that can be used in combination with the arbitrary methods of compression. The method has been studied for the case of its application together with the standard compression method JPEG. The results have shown that, at equal coefficients of compression, the proposed method substantially reduces the maximum error of image restoration. Sergeev Vladislav Viktorovich. Was born in 1951, in 1974 got his degree from the Kuybyshev Aircraft Institute (now the State Aerospace University of Samara). In 1993 he defended his dissertation to the degree of the Doctor of Technical Sciences. At present he is the Head of the Laboratory of Mathematical Methods of Image Processing at the Institute of the Systems of Image Processing of the Russian Academy of Sciences. The circle of his research interests includes digital processing of signals, analysis of images, pattern recognition, geoinformatics. He has over 200 publications, including about 40 papers, two monographs (in collaboration). He is Chairman of the Volga Division of the Russian Association of Pattern Recognition and Image Analysis. He is a Corresponding Member of the Russian Ecological Academy and the Academy of Engineering Sciences of the RF, a member of the SPIE (International Society for Optical Engineering), prize winner of the Samara Administration Prize in Science and Technology. Timbay Yelena Ivanovna. Was born in 1985. In 2008 got her degree from the Samara State Aerospace University (SSAU). At present she is a technician at the Institute of the Image Processing Systems, RAS. She is engaged in image processing, imag compression. Has 11 publication, including 3 articles.     

The Uncertainty Principle: Group Theoretic Approach, Possible Minimizers and Scale-Space Properties

The uncertainty principle is a fundamental concept in the context of signal and image processing, just as much as it has been in the framework of physics and more recently in harmonic analysis. Uncertainty principles can be derived by using a group theoretic approach. This approach yields also a formalism for finding functions which are the minimizers of the uncertainty principles. A general theorem which associates an uncertainty principle with a pair of self-adjoint operators is used in finding the minimizers of the uncertainty related to various groups. This study is concerned with the uncertainty principle in the context of the Weyl-Heisenberg, the SIM(2), the Affine and the Affine-Weyl-Heisenberg groups. We explore the relationship between the two-dimensional affine group and the SIM (2) group in terms of the uncertainty minimizers. The uncertainty principle is also extended to the Affine-Weyl-Heisenberg group in one dimension. Possible minimizers related to these groups are also presented and the scale-space properties of some of the minimizers are explored. Chen Sagiv, finished her B.Sc studies in Physics and Mathematics in 1990 and her M.Sc. studies in Physics in 1995 both in the Tel-Aviv University. After spending a few years in the high-tech industry, she went back to pursue her PhD in Applied Mathematics at the Tel Aviv university. Her main research interests are Gabor analysis and the applications of differential geometry in image processing, especially for texture segmentation. Nir A. Sochen completed his B.Sc. studies in Physics, 1986, and his M.Sc. in theoretical physics, 1988, at the University of Tel-Aviv. He received his Ph.D. in Theoretical physics, 1992, from the Université de Paris-Sud while conducting his research in the Service de Physique Théorique at the Centre d’Etude Nucleaire at Saclay, France. He was the recipient of the Haute Etude Scientifique Fellowship, and pursued his research for one year at the Ecole Normal Superieure, Paris. He was subsequently an NSF Fellow in Physics at the University of California, Berkeley, where focus of research and interest shifted from quantum field theories and integrable models, related to high-energy physics and string theory, to computer vision and image processing. Upon returning to Israel he spent one year with the Physics Dept., Tel-Aviv University and two years with the Department of Electrical Engineering of the Technion. He is currently a Senior Lecturer in the Department of Applied mathematics, Tel-Aviv University, and a member of the Ollendorff Minerva Center, Technion. His main research interests are the applications of differential geometry and statistical physics in image processing and computational vision. Yehoshua Y. (Josh) Zeevi is the Barbara and Norman Seiden Professor of Computer Sciences, Department of Electrical Engineering, Technion, where he served as the Dean 1994–1999. He is the Head of the Ollendorff Minerva Center for Vision and Image Sciences, and of the Zisapel Center for Nano-Electronics. He received his Ph.D. from U.C. Berkeley, was a Visiting Scientist at Lawrence Berkeley Lab; a Vinton Hayes Fellow at Harvard University, a Fellow-at-large at the MIT-NRP, a Visiting Senior Scientist at NTT, an SCEEE Fellow USAF on a joint appointment with MIT, a Visiting Professor at MIT, Harvard, Rutgers and Columbia Universities. His work on automatic gain control in vision led to the development of the Adaptive Sensitivity algorithms and Camera that mimics the eye, and he was a co-founder of i Sight Inc. He was also involved in development of Gabor representations and texture generators for helmet-mounted flight simulators. He is an Editor-in-Chief of J. Visual Communication and Image Representation, Elsevier, and the editor of three books. He has served on boards and international committees, including the Technion Board of Governors and Council, and the IEEE technical committee of Image and Multidimensional Signal Processing.     

Geometric Analysis of Particle Motion in a Vector Image Field

This paper proposes a geometrical model for the Particle Motion in a Vector Image Field (PMVIF) method. The model introduces a c-evolute to approximate the edge curve in the gray-level image. The c-evolute concept has three major novelties: (1) The locus of Particle Motion in a Vector Image Field (PMVIF) is a c-evolute of image edge curve; (2) A geometrical interpretation is given to the setting of the parameters for the method based on the PMVIF; (3) The gap between the image edge’s critical property and the particle motion equations appeared in PMVIF is padded. Our experimental simulation based on the image gradient field is simple in computing and robust, and can perform well even in situations where high curvature exists. Chenggang Lu received his Bachelor of Science and PhD degrees from Zhejiang University in 1996 and 2003, respectively. Since 2003, he has been with VIA Software (Hang Zhou), Inc. and Huawei Technology, Inc. His research interests include image processing, acoustic signaling processing, and communication engineering. Zheru Chi received his BEng and MEng degrees from Zhejiang University in 1982 and 1985 respectively, and his PhD degree from the University of Sydney in March 1994, all in electrical engineering. Between 1985 and 1989, he was on the Faculty of the Department of Scientific Instruments at Zhejiang University. He worked as a Senior Research Assistant/Research Fellow in the Laboratory for Imaging Science and Engineering at the University of Sydney from April 1993 to January 1995. Since February 1995, he has been with the Hong Kong Polytechnic University, where he is now an Associate Professor in the Department of Electronic and Information Engineering. Since 1997, he has served on the organization or program committees for a number of international conferences. His research interests include image processing, pattern recognition, and computational intelligence. Dr. Chi has authored/co-authored one book and nine book chapters, and published more than 140 technical papers. Gang Chen received his Bachelor of Science degree from Anqing Teachers College in 1983 and his PhD degree in the Department of Applied Mathematics at Zhejiang University in 1994. Between 1994 and 1996, he was a postdoctoral researcher in electrical engineering at Zhejiang University. From 1997 to 1999, he was a visiting researcher in the Institute of Mathematics at the Chinese University of Hong Kong and the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University. Since 2001, he has been a Professor at Zhejiang University. He has been the Director of the Institute of DSP and Software Techniques at Ningbo University since 2002. His research interests include applied mathematics, image processing, fractal geometry, wavelet analysis and computer graphics. Prof. Chen has co-authored one book, co-edited five technical proceedings and published more than 80 technical papers. (David) Dagan Feng received his ME in Electrical Engineering & Computing Science (EECS) from Shanghai JiaoTong University in 1982, MSc in Biocybernetics and Ph.D in Computer Science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively. After briefly working as Assistant Professor at the University of California, Riverside, he joined the University of Sydney at the end of 1988, as Lecturer, Senior Lecturer, Reader, Professor and Head of Department of Computer Science/School of Information Technologies, and Associate Dean of Faculty of Science. He is Chair-Professor of Information Technology, Hong Kong Polytechnic University; Honorary Research Consultant, Royal Prince Alfred Hospital, the largest hospital in Australia; Advisory Professor, Shanghai JiaoTong University; Guest Professor, Northwestern Polytechnic University, Northeastern University and Tsinghua University. His research area is Biomedical & Multimedia Information Technology (BMIT). He is the Founder and Director of the BMIT Research Group. He has published over 400 scholarly research papers, pioneered several new research directions, made a number of landmark contributions in his field with significant scientific impact and social benefit, and received the Crump Prize for Excellence in Medical Engineering from USA. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life worldwide. He is a Fellow of ACS, HKIE, IEE, IEEE, and ATSE, Special Area Editor of IEEE Transactions on Information Technology in Biomedicine, and is the current Chairman of IFAC-TC-BIOMED.     

FNS,CFNS and HEIV: A Unifying Approach

Estimation of parameters from image tokens is a central problem in computer vision. FNS, CFNS and HEIV are three recently developed methods for solving special but important cases of this problem. The schemes are means for finding unconstrained (FNS, HEIV) and constrained (CFNS) minimisers of cost functions. In earlier work of the authors, FNS, CFNS and a core version of HEIV were applied to a specific cost function. Here we extend the approach to more general cost functions. This allows the FNS, CFNS and HEIV methods to be placed within a common framework. Wojciech Chojnacki is a professor of mathematics in the Department of Mathematics and Natural Sciences at Cardinal Stefan Wyszyski University in Warsaw. He is concurrently a senior research fellow in the School of Computer Science at the University of Adelaide working on a range of problems in computer vision. His research interests include differential equations, mathematical foundations of computer vision, functional analysis, and harmonic analysis. He is author of over 70 articles on pure mathematics and machine vision, and a member of the Polish Mathematical Society. Michael J. Brooks holds the Chair in Artificial Intelligence within the University of Adelaides School of Computer Science, which he heads. He is also leader of the Image Analysis Program within the Cooperative Research Centre for Sensor Signal and Information Processing, based in South Australia. His research interests include structure from motion, self-calibration, metrology, statistical vision-parameter estimation, and video surveillance and analysis. He is author of over 100 articles on vision, actively involved in a variety of commercial applications, an Associate Editor of the International Journal of Computer Vision, and a Fellow of the Australian Computer Society. Anton van den Hengel is a senior lecturer in the School of Computer Science within the University of Adelaide. He is also leader of the Video Surveillance and Analysis Project within the Cooperative Research Centre for Sensor Signal and Information Processing. His research interests include structure from motion, parameter estimation theory, and commercial applications of computer vision. Darren Gawley graduated with first class honours from the School of Computer Science at the University of Adelaide. He holds a temporary lectureship at the same University, and is currently finalising his PhD in the field of computer vision.This revised version was published online in June 2005 with correction to CoverDate     

Combining Seminorms in Adaptive Lifting Schemes and Applications to Image Analysis and Compression

In this paper, we present some adaptive wavelet decompositions that can capture the directional nature of images. Our method exploits the properties of seminorms to build lifting structures able to choose between different update filters, the choice being triggered by the local gradient-type features of the input. In order to deal with the variety and wealth of images, one has to be able to use multiple criteria, giving rise to multiple choice of update filters. We establish the conditions under these decisions can be recovered at synthesis, without the need for transmitting overhead information. Thus, we are able to design invertible and non-redundant schemes that discriminate between different geometrical information to efficiently represent images for lossless compression methods. The work of Piella is supported by a Marie-Curie Intra-European Fellowships within the 6th European Community Framework Programme. Gemma Piella received the M.S. degree in electrical engineering from the Polytechnical University of Catalonia (UPC), Barcelona, Spain, and the Ph.D. degree from the University of Amsterdam, The Netherlands, in 2003. From 2003 to 2004, she was at UPC as a visiting professor. She then stayed at the Ecole Nationale des Telecommunications, Paris, as a Post-doctoral Fellow. Since September 2005 she is at the Technology Department in the Pompeu Fabra University. Her main research interests include wavelets, geometrical image processing, image fusion and various other aspects of digital image and video processing. Beatrice Pesquet-Popescu received the engineering degree in telecommunications from the “Politehnica” Institute in Bucharest in 1995 and the Ph.D. thesis from the Ecole Normale Supérieure de Cachan in 1998. In 1998 she was a Research and Teaching Assistant at Université Paris XI and in 1999 she joined Philips Research France, where she worked for two years as a research scientist, then project leader, in scalable video coding. Since Oct. 2000 she is an Associate Professor in multimedia at the Ecole Nationale Supérieure des Télécommunications (ENST). Her current research interests are in scalable and robust video coding, adaptive wavelets and multimedia applications. EURASIP gave her a “Best Student Paper Award” in the IEEE Signal Processing Workshop on Higher-Order Statistics in 1997, and in 1998 she received a “Young Investigator Award” granted by the French Physical Society. She is a member of IEEE SPS Multimedia Signal Processing (MMSP) Technical Committee and a Senior Member IEEE. She holds 20 patents in wavelet-based video coding and has authored more than 80 book chapters, journal and conference papers in the field. Henk Heijmans received his masters degree in mathematics from the Technical University in Eindhoven and his PhD degree from the University of Amsterdam in 1985. Since then he has been in the Centre for Mathematics and Computer Science, Amsterdam, where he had been directing the “signals and images” research theme. His research interest are focused towards mathematical techniques for image and signal processing, with an emphasis on mathematical morphology and wavelet analysis. Grégoire Pau was born in Toulouse, France in 1977 and received the M.S. degree in Signal Processing in 2000 from Ecole Centrale de Nantes. From 2000 to 2002, he worked as a Research Engineer at Expway where he actively contributed to the standardization of the MPEG-7 binary format. He is currently a PhD candidate in the Signal and Image Processing Departement of ENST-Telecom Paris. His research interests include subband video coding, motion compensated temporal filtering and adaptive non-linear wavelet transforms.     

Design and implementation of contractual based real-time scheduler for multimedia streaming proxy server

In nowadays World Wide Web topology, it is not difficult to find the presence of proxy servers. They reduce network traffic through the cut down of repetitive information. However, traditional proxy server does not support multimedia streaming. One of the reasons is that general scheduling strategy adopted by most of the traditional proxy servers does not provide real-time support to multimedia services. Based on the concept of contractual scheduling, we have developed a proxy server that supports real-time multimedia applications. Moreover, we developed the group scheduling mechanism to enable processing power transfer between tasks that can hardly be achieved by traditional schedulers. They result in a substantially improved performance particularly when both time-constrained and non-time-constrained processes coexist within the proxy server. In this paper, the design and implementation of this proxy server and the proposed scheduler are detailed. Wai-Kong Cheuk received the B.Eng. (Hons.) and M. Phil. degrees in 1996 and 2001, respectively, from the Hong Kong Polytechnic University, where he is currently pursuing the Ph.D. degree. His main research interests include distributed operating systems and video streaming. Tai-Chiu Hsung (M'93) received the B.Eng. (Hons.) and Ph.D. degrees in electronic and information engineering in 1993 and 1998, respectively, from the Hong Kong Polytechnic University, Hong Kong. In 1999, he joined the Hong Kong Polytechnic University as a Research Fellow. His research interests include wavelet theory and applications, tomography, and fast algorithms. Dr. Hsung is also a member of IEE. Daniel Pak-Kong Lun (M'91) received his B.Sc. (Hons.) degree from the University of Essex, Essex, U.K., and the Ph.D. degree from the Hong Kong Polytechnic University, Hung Hom, Hong Kong, in 1988 and 1991, respectively. He is currently an Associate Professor and the Associate Head of the Department of Electronic and Information Engineering, the Hong Kong Polytechnic University. His research interests include digital signal processing, wavelets, multimedia technology, and Internet technology. Dr. Lun was the Secretary, Treasurer, Vice-Chairman, and Chairman of the IEEE Hong Kong Chapter of Signal Processing in 1994, 1995–1996, 1997–1998, 1999–2000, respectively. He was the Finance Chair of 2003 IEEE International Conference on Acoustics, Speech and Signal Processing, held in Hong Kong, in April 2003. He is a Chartered Engineer and a Corporate member of the IEE.     

Use of Dempster-Shafer theory to combine classifiers which use different class boundaries

In this paper we present the Dempster-Shafer theory as a framework within which the results of a Bayesian network classifier and a fuzzy logic-based classifier are combined to produce a better final classification. We deal with the case when the two original classifiers use different classes for the outcome. The problem of different classes is solved by using a superset of finer classes which can be combined to produce classes according to either of the two classifiers. Within the Dempster-Shafer formalism not only can the problem of different number of classes be solved, but the relative reliability of the classifiers can also be considered. ID="A1"Correspondance and offprint requests to: M. R. Ahmadzadeh, Centre for Vision, Speech and Signal Processing, School of Electronics, Computing and Mathematics, University of Surrey, Guildford, UK     

A New Diffusion-Based Variational Model for Image Denoising and Segmentation

In this paper we propose a new variational model for image denoising and segmentation of both gray and color images. This method is inspired by the complex Ginzburg–Landau model and the weighted bounded variation model. Compared with active contour methods, our new algorithm can detect non-closed edges as well as quadruple junctions, and the initialization is completely automatic. The existence of the minimizer for our energy functional is proved. Numerical results show the effectiveness of our proposed model in image denoising and segmentation. Fang Li received the MSc degree in Mathematics from the South West China Normal University in 2004 and from then on she works in the South West University. Meanwhile, she studies mathematics at the East China Normal University as a doctoral student. Her research interests include anisotropic diffusion filtering, the variational methods and PDEs in image processing. Chaomin Shen received the MSc degree in Mathematics from the National University of Singapore (NUS) in 1998. He worked in the Centre for Remote Imaging, Sensing and Processing (CRISP), NUS as an associate scientist during 1998 to 2004. Currently he is a lecturer in Joint Laboratory for Imaging Science & Technology and Department of Computer Science, East China Normal University. His research interests include remote sensing applications and variational methods in image processing. Ling Pi received her MSc degree from the Department of Mathematics, East China Normal University in 2003. She is currently a lecturer in the Department of Applied Mathematics, Shanghai Jiaotong University. Her work involves the application of geometric and analytic methods to problems in image processing.     

Fuzzy approach for image retrieval

Current technology allows the acquisition, transmission, storing, and manipulation of large collections of images. Content-based information retrieval is now a widely investigated issue that aims at allowing users of multimedia information systems to retrieve images coherent with a sample image. A way to achieve this goal is the automatic computation of features such as color, texture, and shape and the use of these features as query terms. Feature extraction is a crucial part of any such system. Current methods for feature extraction suffer from two main problems: firstly, many methods do not retain any spatial information, and secondly, the problem of invariance with respect to standard transformation is still unsolved. In this paper, we describe some results of a study on similarity evaluation in image retrieval using shape, texture, and color as content features. Images are retrieved based on similarity of features, where features of the query specification are compared with features of the image database to determine which images match similarly with given features. In this paper, we propose an effective method for image representation which utilizes fuzzy features. The text was submitted by the author in English. Ryszard S. Choraś is Professor of Computer Science in the Department of Telecommunications and EE of University of Technology and Agriculture, Bydgoszcz, Poland. He also holds a courtesy appointment with the Faculty of Mathematics, Technology, and Natural Sciences of Kazimierz Wielki University, Bydgoszcz and the College of Computer Science, Lódz, Poland. His research interests include image signal compression and coding, computer vision, and multimedia data transmission. He received his M.S. degree in Electrical Engineering from Electronics from the Technical University of Wroclaw, Poland in 1973, and his Ph.D. degree in Electronics from Technical University of Wroclaw, Poland, in 1980, and D.Sc. (Habilitation degree) in Computer Science from Warsaw Technical University, Poland, in 1993. Until 1973–1976 he was a member of the research staff at the Institute of Mathematical Machines Silesian Division, Gliwice, working on graphics hardware and human visual perception. In 1976, he joined University of Technology and Agriculture, Bydgoszcz, Poland, first as an Assistant, then as a Professor of Computer Science at the Department of Telecommunications and EE. From 1994 to 1996, he was also Professor of Computer Sciences of the Zielona Góra University, Poland. He has served as the Chairman of the Communication Switching Division and as Chief of the Image Processing and Recognition Group. Until 1996–2002 he was the Vice Rector of University of Technology and Agriculture, Bydgoszcz. Prof. Choraś has an expertise in EU Programs and National Programs, e.g., he was coordinator of EU Program CME-02060, EU Program on Continuous Education and Technology Transfer, and coordinator of national programs in IST and multimedia in e-learning. Prof. Choraś has authored two monographs, and over 130 book chapters, journal articles, and conference papers in the area of image processing. Professor Choraś is a member of the editorial boards of “Machine Vision and Graphics.” He is the editor-in-chief of “Image Processing and Communications Journal.” He has served on numerous conference committees, e.g., Visualization, Imaging, and Image Processing (VIIP), IASTED International Conference on Signal Processing, Pattern Recognition and Applications, International Conference on Computer Vision and Graphics, ICINCO International Conference on Informatics in Control, Automation and Robotics, ICETE International Conference on E-business and Telecommunication Networks, and CORES International Conference on Computer Recognition Systems, and many others. Prof Choraś is a member of the IASTED, WSEAS, various Committees of the Polish Academy of Sciences, TPO. When not working on academic ventures, Professor Choraś likes to relax with activities such as walking, tennis, and swimming.     

Fast correlation method with the use of a wide selection of template in problems of image analysis

The problem of searching for and recognizing fragments of images that correspond to one of a wide variety of template is considered. The method of the fast correlation of a wide selection of trinary template, which successfully resolves this problem, is suggested. The use of this method in two problems of image analysis is shown, namely, the search for position of eyes in documental photographs of faces and the recognition of computer-readable lines in scanned images of documents. Nikolai Ivanovich Glumov. Born in 1962. In 1985, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. His circle of scientific interests involves the image processing and pattern recognition, image compression, and simulation of the systems of formation of digital images. Glumov has more than 90 publications involving more than 30 articles and one monograph (in partnership). He is a member of the Russian Association of Pattern Recognition and Image Processing. Evgenii Valer’evich Myasnikov. Born in 1981. In 2004, he graduated the Samara State Aerospace University and entered the Post-Graduate Education of SGAU. In 2007, Myasnikov defended the Candidate of Science (Engineering) Dissertation. Currently, is working as the Probationer Researcher at the Image Processing Systems Institute, Russian Academy of Sciences and simultaneously as the Assistant of the Department of Geoinformatics at SCAU. The circle of scientific interests involves the creation of software complexes, image processing and pattern recognition, and search for images in databases. Myasnikov has 23 publications, including six articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Vasilii Nikolaevich Kopenkov. Born in 1978. In 2001, he graduated the Samara State Aerospace University (SGAU). Currently, he is working as the assistant of the Department of Geoinformatics at the SGAU and Junior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. The circle of scientific interests involves the processing of images of the distanced probing of the Earth, pattern recognition, and geoinformatic systems. Kopenkov has 17 publications, including seven articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Marina Aleksandrovna Chicheva. Born in 1964. In 1987, she graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1998, she defended the Candidate of Science (Engineering) Dissertation. She currently works as the Senior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Her scientific interests include image processing and compression, rapid algorithms of discrete transformations, and pattern recognition. Chicheva has more than 18 articles, including one monograph (in partnership). She is the member of the Russian Association of Pattern Recognition and Image Processing.     

A machine vision system for quantifying velocity fields in complex rock models

In this paper we describe a machine vision system capable of high-resolution measurement of fluid velocity fields in complex 2D models of rock, providing essential data for the validation of the numerical models which are widely applied in the oil and petroleum industries. Digital models, incorporating the properties of real rock, are first generated, then physically replicated as layers of resin or aluminium (200 mm × 200 mm) encapsulated between transparent plates as a flowcell. This configuration enables the geometry to be permeated with fluid and fluid motion visualised using particle image velocimetry. Fluid velocity fields are then computed using well-tested cross-correlation techniques. Rachel Cassidy is a Research Associate in Geophysics at the University of Ulster. Dr. Cassidy's research interests include percolation theory and its application to fluid flow in fractured rock, the fractal and multifractal properties of natural phenomena and the development of experimental techniques for investigating fluid flow in porous fractured media with realistic structure and exhibiting scale invariance. She is currently involved in the development of molecular tracer techniques for characterising reservoir heterogeneity. Philip Morrow is currently a Senior Lecturer in the School of Computing and Information Engineering at the University of Ulster. Dr. Morrow has a BSc in Applied Mathematics and Computer Science, an MSc in Electronics and a PhD in Parallel Image Processing, all from the Queen's University of Belfast. His main research interests lie in image processing, computer vision and parallel/distributed computing. He has published over 65 research papers in these areas. John McCloskey is Professor of Geophysics and Head of the School of Environmental Sciences at the University of Ulster. Prof. McCloskey's research interests are in the application of ideas of chaos and complexity to a variety of geophysical problems including earthquake dynamics and fluid flow in fractured porous rock. He has published over 100 articles and is a regular contributor to international press on matters connected with earth science.