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1.
Maria Petrou Alireza Talebpour Alexander Kadyrov 《Pattern Analysis & Applications》2007,10(2):101-114
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.
Maria Petrou studied Physics at the Aristotle University of Thessaloniki, Greece, Applied Mathematics in Cambridge and she did her Ph.D. in the Institute of Astronomy in Cambridge, UK. She is currently the Professor of Signal Processing and the Head of the Communications and Signal Processing Group at Imperial College. She has published more than 300 scientific papers, on Astronomy, Remote Sensing, Computer Vision, Machine Learning, Colour analysis, Industrial Inspection, and Medical Signal and Image Processing. She has co-authored two books “Image Processing: the fundamentals” and “Image Processing: Dealing with texture” both published by John Wiley in 1999 and 2006, respectively. She is a Fellow of the Royal Academy of Engineering, Fellow of IEE, Fellow of IAPR, Senior member of IEEE and a Distinguished Fellow of the British Machine Vision Association. Alireza Talebpour worked for several years in the private sector after his first degree in Electrical Engineering in Iran. He obtained his Ph.D. in image processing from Surrey University in 2004, and since then he has been a lecturer at Shahid Beheshti University in Iran. His research interests are in multimedia and signal and image processing. Alexander Kadyrov obtained his Ph.D. in Mathematics, in 1983 from St Petersburg University. From 1979 to 1997 he held various research and teaching positions at Penza State University, Russia. He started working on computer vision in 1998. He has authored or co-authored about 60 papers, textbooks and inventions. 相似文献
Maria PetrouEmail: |
Maria Petrou studied Physics at the Aristotle University of Thessaloniki, Greece, Applied Mathematics in Cambridge and she did her Ph.D. in the Institute of Astronomy in Cambridge, UK. She is currently the Professor of Signal Processing and the Head of the Communications and Signal Processing Group at Imperial College. She has published more than 300 scientific papers, on Astronomy, Remote Sensing, Computer Vision, Machine Learning, Colour analysis, Industrial Inspection, and Medical Signal and Image Processing. She has co-authored two books “Image Processing: the fundamentals” and “Image Processing: Dealing with texture” both published by John Wiley in 1999 and 2006, respectively. She is a Fellow of the Royal Academy of Engineering, Fellow of IEE, Fellow of IAPR, Senior member of IEEE and a Distinguished Fellow of the British Machine Vision Association. Alireza Talebpour worked for several years in the private sector after his first degree in Electrical Engineering in Iran. He obtained his Ph.D. in image processing from Surrey University in 2004, and since then he has been a lecturer at Shahid Beheshti University in Iran. His research interests are in multimedia and signal and image processing. Alexander Kadyrov obtained his Ph.D. in Mathematics, in 1983 from St Petersburg University. From 1979 to 1997 he held various research and teaching positions at Penza State University, Russia. He started working on computer vision in 1998. He has authored or co-authored about 60 papers, textbooks and inventions. 相似文献
2.
Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images 总被引:1,自引:1,他引:0
Yangqiu Song Changshui Zhang Jianguo Lee Fei Wang Shiming Xiang Dan Zhang 《Pattern Analysis & Applications》2009,12(2):99-115
Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work
is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous
tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian
Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled
data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach
produces satisfactory segmentation results comparing to the manually labeled results by experts.
Yangqiu Song received his B.S. degree from Department of Automation, Tsinghua University, China, in 2003. He is currently a Ph.D. candidate in Department of Automation, Tsinghua University. His research interests focus on machine learning and its applications. Changshui Zhang received his B.S. degree in Mathematics from Peking University, China, in 1986, and Ph.D. degree from Department of Automation, Tsinghua University in 1992. He is currently a professor of Department of Automation, Tsinghua University. He is an Associate Editor of the journal Pattern Recognition. His interests include artificial intelligence, image processing, pattern recognition, machine learning, evolutionary computation and complex system analysis, etc. Jianguo Lee received his B.S. degree from Department of Automatic Control, Huazhong University of Science and Technology (HUST), China, in 2001 and Ph.D. degree in Department of Automation, Tsinghua University in 2006. He is currently a researcher in Intel China Reasearch Center. His research interests focus on machine learning and its applications. Fei Wang is a Ph.D. candidate from Department of Automation, Tsinghua University, Beijing, China. His main research interests include machine learning, data mining, and pattern recognition. Shiming Xiang received his B.S. degree from Department of Mathematics of Chongqing Normal University, China, in 1993 and M.S. degree from Department of Mechanics and Mathematics of Chongqing University, China, in 1996 and Ph.D. degree from Institute of Computing Technology, Chinese Academy of Sciences, China, in 2004. He is currently a postdoctoral scholar in Department of Automation, Tsinghua University. His interests include computer vision, pattern recognition, machine learning, etc. Dan Zhang received his B.S. degree in Electronic and Information Engineering from Nanjing University of Posts and Telecommunications in 2005. He is now a Master candidate from Department of Automation, Tsinghua University, Beijing, China. His research interests include pattern recognition, machine learning, and blind signal separation. 相似文献
Changshui ZhangEmail: |
Yangqiu Song received his B.S. degree from Department of Automation, Tsinghua University, China, in 2003. He is currently a Ph.D. candidate in Department of Automation, Tsinghua University. His research interests focus on machine learning and its applications. Changshui Zhang received his B.S. degree in Mathematics from Peking University, China, in 1986, and Ph.D. degree from Department of Automation, Tsinghua University in 1992. He is currently a professor of Department of Automation, Tsinghua University. He is an Associate Editor of the journal Pattern Recognition. His interests include artificial intelligence, image processing, pattern recognition, machine learning, evolutionary computation and complex system analysis, etc. Jianguo Lee received his B.S. degree from Department of Automatic Control, Huazhong University of Science and Technology (HUST), China, in 2001 and Ph.D. degree in Department of Automation, Tsinghua University in 2006. He is currently a researcher in Intel China Reasearch Center. His research interests focus on machine learning and its applications. Fei Wang is a Ph.D. candidate from Department of Automation, Tsinghua University, Beijing, China. His main research interests include machine learning, data mining, and pattern recognition. Shiming Xiang received his B.S. degree from Department of Mathematics of Chongqing Normal University, China, in 1993 and M.S. degree from Department of Mechanics and Mathematics of Chongqing University, China, in 1996 and Ph.D. degree from Institute of Computing Technology, Chinese Academy of Sciences, China, in 2004. He is currently a postdoctoral scholar in Department of Automation, Tsinghua University. His interests include computer vision, pattern recognition, machine learning, etc. Dan Zhang received his B.S. degree in Electronic and Information Engineering from Nanjing University of Posts and Telecommunications in 2005. He is now a Master candidate from Department of Automation, Tsinghua University, Beijing, China. His research interests include pattern recognition, machine learning, and blind signal separation. 相似文献
3.
This paper
focuses on human behavior recognition where the main problem is to bridge the
semantic gap between the analogue observations of the real world and the
symbolic world of human interpretation. For that, a fusion architecture based
on the Transferable Belief Model framework is proposed and applied to action
recognition of an athlete in video sequences of athletics meeting with moving
camera. Relevant features are extracted from videos, based on both the camera
motion analysis and the tracking of particular points on the athlete’s
silhouette. Some models of interpretation are used to link the numerical
features to the symbols to be recognized, which are running, jumping and
falling actions. A Temporal Belief Filter is then used to improve the
robustness of action recognition. The proposed approach demonstrates good
performance when tested on real videos of athletics sports videos (high jumps,
pole vaults, triple jumps and long jumps) acquired by a moving camera and
different view angles. The proposed system is also compared to Bayesian
Networks.
Emmanuel Ramasso is currently pursuing a PhD at GIPSA-lab, Department of Images and Signal located in Grenoble, France. He received both his BS degree in Electrical Engineering and Control Theory and his MS degree in Computer Science in 2004 from Ecole Polytechnique de Savoie (Annecy, France). His research interests include Sequential Data Analysis, Transferable Belief Model, Fusion, Image and Videos Analysis and Human Motion Analysis. Costas Panagiotakis was born in Heraklion, Crete, Greece in 1979. He received the BS and the MS degrees in Computer Science from University of Crete in 2001 and 2003, respectively. Currently, he is a PhD candidate in Computer Science at University of Crete. His research interests include computer vision, image and video analysis, motion analysis and synthesis, computer graphics, computational geometry and signal processing. Denis Pellerin received the Engineering degree in Electrical Engineering in 1984 and the PhD degree in 1988 from the Institut National des Sciences Appliquées, Lyon, France. He is currently a full Professor at the Université Joseph Fourier, Grenoble, France. His research interests include visual perception, motion analysis in image sequences, video analysis, and indexing. Michèle Rombaut is currently a full Professor at the Université Joseph Fourier, Grenoble, France. Her research interests include Data Fusion, Sequential Data Analysis, High Level Interpretation, Image and Video Analysis. 相似文献
M. RombautEmail: |
Emmanuel Ramasso is currently pursuing a PhD at GIPSA-lab, Department of Images and Signal located in Grenoble, France. He received both his BS degree in Electrical Engineering and Control Theory and his MS degree in Computer Science in 2004 from Ecole Polytechnique de Savoie (Annecy, France). His research interests include Sequential Data Analysis, Transferable Belief Model, Fusion, Image and Videos Analysis and Human Motion Analysis. Costas Panagiotakis was born in Heraklion, Crete, Greece in 1979. He received the BS and the MS degrees in Computer Science from University of Crete in 2001 and 2003, respectively. Currently, he is a PhD candidate in Computer Science at University of Crete. His research interests include computer vision, image and video analysis, motion analysis and synthesis, computer graphics, computational geometry and signal processing. Denis Pellerin received the Engineering degree in Electrical Engineering in 1984 and the PhD degree in 1988 from the Institut National des Sciences Appliquées, Lyon, France. He is currently a full Professor at the Université Joseph Fourier, Grenoble, France. His research interests include visual perception, motion analysis in image sequences, video analysis, and indexing. Michèle Rombaut is currently a full Professor at the Université Joseph Fourier, Grenoble, France. Her research interests include Data Fusion, Sequential Data Analysis, High Level Interpretation, Image and Video Analysis. 相似文献
4.
NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear
discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines
the two methods for face recognition. We first project the face images into a PCA subspace, then project the results into
a much lower-dimensional LDA subspace, and then use an NNSRM classifier to recognize them in the LDA subspace. Experimental
results demonstrate that the combined method can achieve a better performance than NN by selecting different distances and
a comparable performance with SVM but costing less computational time.
Danian Zheng received his Bachelor degree in Computer Science and Technology in 2002 from Tsinghua University, Beijing, China. He received his Master degree and Doctoral degree in Computer Science and Technology in 2006 from Tsinghua University. He is currently a researcher in Fujitsu R&D Center Co. Ltd, Beijing, China. His research interests are mainly in the areas of support vector machines, kernel methods and their applications. Meng Na received her Bachelor degree in Computer Science and Technology in 2003 from Northeastern, China. Since 2003 she has been pursuing the Master degree and the Doctoral degree at the Department of Computer Science and Technology at Tsinghua University. Her research interests are in the area of image processing, pattern recognition, and virtual human. Jiaxin Wang received his Bachelor degree in Automatic Control in 1965 from Beijing University of Aeronautics and Astronautics, his Master degree in Computer Science and Technology in 1981 from Tsinghua University, Beijing, China, and his Doctoral degree in 1996 from Engineering Faculty of Vrije Universiteit Brussel, Belgium. He is currently a professor of Department of Computer Science and Technology, Tsinghua University. His research interests are in the areas of artificial intelligence, intelligent control and robotics, machine learning, pattern recognition, image processing and virtual reality. 相似文献
Jiaxin Wang (Corresponding author)Email: |
Danian Zheng received his Bachelor degree in Computer Science and Technology in 2002 from Tsinghua University, Beijing, China. He received his Master degree and Doctoral degree in Computer Science and Technology in 2006 from Tsinghua University. He is currently a researcher in Fujitsu R&D Center Co. Ltd, Beijing, China. His research interests are mainly in the areas of support vector machines, kernel methods and their applications. Meng Na received her Bachelor degree in Computer Science and Technology in 2003 from Northeastern, China. Since 2003 she has been pursuing the Master degree and the Doctoral degree at the Department of Computer Science and Technology at Tsinghua University. Her research interests are in the area of image processing, pattern recognition, and virtual human. Jiaxin Wang received his Bachelor degree in Automatic Control in 1965 from Beijing University of Aeronautics and Astronautics, his Master degree in Computer Science and Technology in 1981 from Tsinghua University, Beijing, China, and his Doctoral degree in 1996 from Engineering Faculty of Vrije Universiteit Brussel, Belgium. He is currently a professor of Department of Computer Science and Technology, Tsinghua University. His research interests are in the areas of artificial intelligence, intelligent control and robotics, machine learning, pattern recognition, image processing and virtual reality. 相似文献
5.
FRCT: fuzzy-rough classification trees 总被引:1,自引:1,他引:0
Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition
attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure
proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision
tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree
structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm
has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation
algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules,
average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT
outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.
Dr. Rajen Bhatt has obtained his B.E. and M.E. both in Control and Instrumentation, from S.S. Engineering College, Bhavnagar, and from Delhi College of Engineering, New Delhi in 1999 and 2002, respectively. He has obtained his Ph.D. from the Department of Electrical Engineering, Indian Institute of Technology Delhi, INDIA in 2006. He was actively engaged in the development of multimedia course on Control Engineering under the National Program on Technology Enabled Learning (NPTEL). He is a regular reviewer of International Journals like Pattern Recognition, Information Sciences, Pattern Analysis and Applications, and IEEE Trans. on Systems, Man and Cybernatics. Since June 2005, he is working with Imaging team of Samsung India Software Centre as a Lead Engineer. He also serves as a Member of Patent Review Committee at Samsung. He has published several research papers in reputed journals and conferences. His current research interests are Pattern Classification and Regression, Soft Computing, Data mining, Patents and Trademarks, and Information Technology for Education. He holds an expertise over industry standard software project management. Dr. M. Gopal has obtained his B.Tech. (Electrical), M.Tech. (Control systems), and Ph.D. (Control Systems) degrees. all from Birla Institute of Technology and Science, Pilani in 1968, 1970, and 1976, respectively. He has been in the teaching and research field for the last three and half decades; associated with NIT Jaipur, BITS Pilani, IIT Bombay, City University London, and University Technology Malaysia, and IIT Delhi. Since January 1986 he is a Professor with the Electrical Engineering Department, Indian Institute of Technology Delhi. He has published six books in the area of Control Engineering, and a video course on Control Engineering including complete presentation and student questionnaires. He has also published interactive web-compatible multimedia course on Control Engineering, under National Program on Technology Enabled Learning (NPTEL). He has published several research papers in referred journals and conferences. His current research interests include Machine learning, Soft computing technologies, Intelligent control, and e-Learning. 相似文献
Rajen B. BhattEmail: |
Dr. Rajen Bhatt has obtained his B.E. and M.E. both in Control and Instrumentation, from S.S. Engineering College, Bhavnagar, and from Delhi College of Engineering, New Delhi in 1999 and 2002, respectively. He has obtained his Ph.D. from the Department of Electrical Engineering, Indian Institute of Technology Delhi, INDIA in 2006. He was actively engaged in the development of multimedia course on Control Engineering under the National Program on Technology Enabled Learning (NPTEL). He is a regular reviewer of International Journals like Pattern Recognition, Information Sciences, Pattern Analysis and Applications, and IEEE Trans. on Systems, Man and Cybernatics. Since June 2005, he is working with Imaging team of Samsung India Software Centre as a Lead Engineer. He also serves as a Member of Patent Review Committee at Samsung. He has published several research papers in reputed journals and conferences. His current research interests are Pattern Classification and Regression, Soft Computing, Data mining, Patents and Trademarks, and Information Technology for Education. He holds an expertise over industry standard software project management. Dr. M. Gopal has obtained his B.Tech. (Electrical), M.Tech. (Control systems), and Ph.D. (Control Systems) degrees. all from Birla Institute of Technology and Science, Pilani in 1968, 1970, and 1976, respectively. He has been in the teaching and research field for the last three and half decades; associated with NIT Jaipur, BITS Pilani, IIT Bombay, City University London, and University Technology Malaysia, and IIT Delhi. Since January 1986 he is a Professor with the Electrical Engineering Department, Indian Institute of Technology Delhi. He has published six books in the area of Control Engineering, and a video course on Control Engineering including complete presentation and student questionnaires. He has also published interactive web-compatible multimedia course on Control Engineering, under National Program on Technology Enabled Learning (NPTEL). He has published several research papers in referred journals and conferences. His current research interests include Machine learning, Soft computing technologies, Intelligent control, and e-Learning. 相似文献
6.
Vasileios K. Pothos Christos Theoharatos Evangelos Zygouris George Economou 《Pattern Analysis & Applications》2008,11(2):117-129
Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying
texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate
Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of
multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using
standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different
images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality
and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture
database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution
(dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even
though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.
Vasileios K. Pothos received the B.Sc. degree in Physics in 2004 and the M.Sc. degree in Electronics and Information Processing in 2006, both from the University of Patras (UoP), Greece. He is currently a Ph.D. candidate in image processing at the Electronics Laboratory in the Department of Physics, UoP, Greece. His main research interests include image processing, pattern recognition and multimedia databases. Dr. Christos Theoharatos received the B.Sc. degree in Physics in 1998, the M.Sc. degree in Electronics and Computer Science in 2001 and the Ph.D. degree in Image Processing and Multimedia Retrieval in 2006, all from the University of Patras (UoP), Greece. He has actively participated in several national research projects and is currently working as a PostDoc researcher at the Electronics Laboratory (ELLAB), Electronics and Computer Division, Department of Physics, UoP. Since the academic year 2002, he has been working as tutor at the degree of lecturer in the Department of Electrical Engineering, of the Technological Institute of Patras. His main research interests include pattern recognition, multimedia databases, image processing and computer vision, data mining and graph theory. Prof. Evangelos Zygouris received the B.Sc. degree in Physics in 1971 and the Ph.D. degree in Digital Filters and Microprocessors in 1984, both from the University of Patras (UoP), Greece. He is currently an Associate Professor at Electronics Laboratory (ELLAB), Department of Physics, UoP, where he teaches at both undergraduate and postgraduate level. He has published papers on digital signal and image processing, digital system design, speech coding systems and real-time processing. His main research interests include digital signal and image processing, DSP system design, micro-controllers, micro-processors and DSPs using VHDL. Prof. George Economou received the B.Sc. degree in Physics from the University of Patras (UoP), Greece in 1976, the M.Sc. degree in Microwaves and Modern Optics from University College London in 1978 and the Ph.D. degree in Fiber Optic Sensor Systems from the University of Patras in 1989. He is currently an Associate Professor at Electronics Laboratory (ELLAB), Department of Physics, UoP, where he teaches at both undergraduate and postgraduate level. He has published papers on non-linear signal and image processing, fuzzy image processing, multimedia databases, data mining and fiber optic sensors. He has also served as referee for many journals, conferences and workshops. His main research interests include signal and image processing, computer vision, pattern recognition and optical signal processing. 相似文献
George EconomouEmail: |
Vasileios K. Pothos received the B.Sc. degree in Physics in 2004 and the M.Sc. degree in Electronics and Information Processing in 2006, both from the University of Patras (UoP), Greece. He is currently a Ph.D. candidate in image processing at the Electronics Laboratory in the Department of Physics, UoP, Greece. His main research interests include image processing, pattern recognition and multimedia databases. Dr. Christos Theoharatos received the B.Sc. degree in Physics in 1998, the M.Sc. degree in Electronics and Computer Science in 2001 and the Ph.D. degree in Image Processing and Multimedia Retrieval in 2006, all from the University of Patras (UoP), Greece. He has actively participated in several national research projects and is currently working as a PostDoc researcher at the Electronics Laboratory (ELLAB), Electronics and Computer Division, Department of Physics, UoP. Since the academic year 2002, he has been working as tutor at the degree of lecturer in the Department of Electrical Engineering, of the Technological Institute of Patras. His main research interests include pattern recognition, multimedia databases, image processing and computer vision, data mining and graph theory. Prof. Evangelos Zygouris received the B.Sc. degree in Physics in 1971 and the Ph.D. degree in Digital Filters and Microprocessors in 1984, both from the University of Patras (UoP), Greece. He is currently an Associate Professor at Electronics Laboratory (ELLAB), Department of Physics, UoP, where he teaches at both undergraduate and postgraduate level. He has published papers on digital signal and image processing, digital system design, speech coding systems and real-time processing. His main research interests include digital signal and image processing, DSP system design, micro-controllers, micro-processors and DSPs using VHDL. Prof. George Economou received the B.Sc. degree in Physics from the University of Patras (UoP), Greece in 1976, the M.Sc. degree in Microwaves and Modern Optics from University College London in 1978 and the Ph.D. degree in Fiber Optic Sensor Systems from the University of Patras in 1989. He is currently an Associate Professor at Electronics Laboratory (ELLAB), Department of Physics, UoP, where he teaches at both undergraduate and postgraduate level. He has published papers on non-linear signal and image processing, fuzzy image processing, multimedia databases, data mining and fiber optic sensors. He has also served as referee for many journals, conferences and workshops. His main research interests include signal and image processing, computer vision, pattern recognition and optical signal processing. 相似文献
7.
Optimizing two-pass connected-component labeling algorithms 总被引:5,自引:0,他引:5
We present two optimization strategies to improve connected-component labeling algorithms. Taking together, they form an efficient
two-pass labeling algorithm that is fast and theoretically optimal. The first optimization strategy reduces the number of
neighboring pixels accessed through the use of a decision tree, and the second one streamlines the union-find algorithms used
to track equivalent labels. We show that the first strategy reduces the average number of neighbors accessed by a factor of
about 2. We prove our streamlined union-find algorithms have the same theoretical optimality as the more sophisticated ones
in literature. This result generalizes an earlier one on using union-find in labeling algorithms by Fiorio and Gustedt (Theor
Comput Sci 154(2):165–181, 1996). In tests, the new union-find algorithms improve a labeling algorithm by a factor of 4 or
more. Through analyses and experiments, we demonstrate that our new two-pass labeling algorithm scales linearly with the number
of pixels in the image, which is optimal in computational complexity theory. Furthermore, the new labeling algorithm outperforms
the published labeling algorithms irrespective of test platforms. In comparing with the fastest known labeling algorithm for
two-dimensional (2D) binary images called contour tracing algorithm, our new labeling algorithm is up to ten times faster
than the contour tracing program distributed by the original authors.
Kesheng Wu is a staff computer scientist at Lawrence Berkeley National Laboratory. His work primarily involves data management, data analyses and scientific computing. He is the lead developer of FastBit bitmap indexing software for searching over large datasets. He also led the development of a software package call TRLan, which computes eigenvalues of large symmetric matrices on parallel machines. He received a Ph.D. in computer science from the University of Minnesota, an M.S. in physics from the University of Wisconsin-Milwaukee, and a B.S. in physics from Nanjing University, China. His homepage on the web is . Ekow Otoo holds a B.Sc. degree in Electrical Engineering from the University of Science and Technology, Kumasi, Ghana, and a Ph.D. degree in Computer Science from McGill University, Montreal, Canada. From 1987 to 1999, he was a tenured faculty at Carleton University, Ottawa, Canada. He has served as a consultant to Bell Northern Research, and the GIS Division, Geomatics Canada. He is presently a consultant with Mathematical Sciences Research Institute, Ghana, and a staff scientist/engineer, LBNL, Berkeley. He is a member of the ACM and IEEE. His research interests include database management, data structures, algorithms, parallel and distributed computing. Kenji Suzuki received his Ph.D. degree from Nagoya University in 2001. In 2001, he joined Department of Radiology at University of Chicago. Since 2006, he has been Assistant Professor of Radiology, Medical Physics, and Cancer Research Center. His research interests include computer-aided diagnosis, machine learning, and pattern recognition. He published 110 papers including 45 journal papers. He has served as an associate editor for three journals and a referee for 17 journals. He received Paul Hodges Award, RSNA Certificate of Merit Awards, Cancer Research Foundation Young Investigator Award, and SPIE Honorable Mention Award. He is a Senior Member of IEEE. 相似文献
Kenji SuzukiEmail: |
Kesheng Wu is a staff computer scientist at Lawrence Berkeley National Laboratory. His work primarily involves data management, data analyses and scientific computing. He is the lead developer of FastBit bitmap indexing software for searching over large datasets. He also led the development of a software package call TRLan, which computes eigenvalues of large symmetric matrices on parallel machines. He received a Ph.D. in computer science from the University of Minnesota, an M.S. in physics from the University of Wisconsin-Milwaukee, and a B.S. in physics from Nanjing University, China. His homepage on the web is . Ekow Otoo holds a B.Sc. degree in Electrical Engineering from the University of Science and Technology, Kumasi, Ghana, and a Ph.D. degree in Computer Science from McGill University, Montreal, Canada. From 1987 to 1999, he was a tenured faculty at Carleton University, Ottawa, Canada. He has served as a consultant to Bell Northern Research, and the GIS Division, Geomatics Canada. He is presently a consultant with Mathematical Sciences Research Institute, Ghana, and a staff scientist/engineer, LBNL, Berkeley. He is a member of the ACM and IEEE. His research interests include database management, data structures, algorithms, parallel and distributed computing. Kenji Suzuki received his Ph.D. degree from Nagoya University in 2001. In 2001, he joined Department of Radiology at University of Chicago. Since 2006, he has been Assistant Professor of Radiology, Medical Physics, and Cancer Research Center. His research interests include computer-aided diagnosis, machine learning, and pattern recognition. He published 110 papers including 45 journal papers. He has served as an associate editor for three journals and a referee for 17 journals. He received Paul Hodges Award, RSNA Certificate of Merit Awards, Cancer Research Foundation Young Investigator Award, and SPIE Honorable Mention Award. He is a Senior Member of IEEE. 相似文献
8.
A serial multi-stage classification system for facing the problem of intrusion detection in computer networks is proposed.
The whole decision process is organized into successive stages, each one using a set of features tailored for recognizing
a specific attack category. All the stages employ suitable criteria for estimating the reliability of the performed classification,
so that, in case of uncertainty, information related to a possible attack are only logged for further processing, without
raising an alert for the system manager. This permits to reduce the number of false alarms. On the other hand, in order to
keep low the number of missed detections, the proposed system declares a connection as normal traffic only if all the stages
do not detect an attack. The proposed multi-stage intrusion detection system has been tested on three different services (http, telnet and ftp) of a standard database used for benchmarking intrusion detection systems and also on real network traffic data. The experimental
analysis highlights the effectiveness of the approach: the proposed system behaves significantly better than other multiple
classifier systems performing classification in a single stage.
Luigi Pietro Cordella is a Professor of Computer Science at the Faculty of Engineering of the University of Naples “Federico II” (Italy). He has been Chairman of the Department of Computer Science and Systems and, since 1994, Chairman of the Ph.D. course program in Information Engineering of the University of Naples. His present research interests include Syntactic and Structural Pattern Recognition, Shape Analysis, Document Recognition, OCR, Neural Networks, and Evolutionary Computation. He has published over 150 papers and is editor or co-editor of six books. He is a Fellow of IAPR and a member of IEEE and Computer Society. He has been President of GIRPR (2000–2004), the Italian Association for Pattern Recognition, and member of the Governing Board of the IAPR. Carlo Sansone is Associate Professor of Computer Science at the Faculty of Engineering of the University of Naples “Federico II” (Italy). His research principally focuses on classification techniques, exact and inexact graph matching and multiple-classifier systems theory and applications. He coordinated several projects in the areas of car plate recognition, biomedical images interpretation and network intrusion detection. Prof. Sansone has authored about 90 research papers in international journals and conference proceedings. He serves as referee for many relevant journals in the field of Pattern Recognition and is Associate editor of the Electronic Letters on Computer Vision and Image Analysis journal. He is currently co-editor of a special issue on “Information Fusion in Computer Security” for the Information Fusion journal. 相似文献
Carlo Sansone (Corresponding author)Email: |
Luigi Pietro Cordella is a Professor of Computer Science at the Faculty of Engineering of the University of Naples “Federico II” (Italy). He has been Chairman of the Department of Computer Science and Systems and, since 1994, Chairman of the Ph.D. course program in Information Engineering of the University of Naples. His present research interests include Syntactic and Structural Pattern Recognition, Shape Analysis, Document Recognition, OCR, Neural Networks, and Evolutionary Computation. He has published over 150 papers and is editor or co-editor of six books. He is a Fellow of IAPR and a member of IEEE and Computer Society. He has been President of GIRPR (2000–2004), the Italian Association for Pattern Recognition, and member of the Governing Board of the IAPR. Carlo Sansone is Associate Professor of Computer Science at the Faculty of Engineering of the University of Naples “Federico II” (Italy). His research principally focuses on classification techniques, exact and inexact graph matching and multiple-classifier systems theory and applications. He coordinated several projects in the areas of car plate recognition, biomedical images interpretation and network intrusion detection. Prof. Sansone has authored about 90 research papers in international journals and conference proceedings. He serves as referee for many relevant journals in the field of Pattern Recognition and is Associate editor of the Electronic Letters on Computer Vision and Image Analysis journal. He is currently co-editor of a special issue on “Information Fusion in Computer Security” for the Information Fusion journal. 相似文献
9.
A practical approach to testing GUI systems 总被引:1,自引:0,他引:1
GUI systems are becoming increasingly popular thanks to their ease of use when compared against traditional systems. However,
GUI systems are often challenging to test due to their complexity and special features. Traditional testing methodologies
are not designed to deal with the complexity of GUI systems; using these methodologies can result in increased time and expense.
In our proposed strategy, a GUI system will be divided into two abstract tiers—the component tier and the system tier. On
the component tier, a flow graph will be created for each GUI component. Each flow graph represents a set of relationships
between the pre-conditions, event sequences and post-conditions for the corresponding component. On the system tier, the components
are integrated to build up a viewpoint of the entire system. Tests on the system tier will interrogate the interactions between
the components. This method for GUI testing is simple and practical; we will show the effectiveness of this approach by performing
two empirical experiments and describing the results found.
Ping Li received her M.Sc. in Computer Engineering from the University of Alberta, Canada, in 2004. She is currently working for Waterloo Hydrogeologic Inc., a Schlumberger Company, as a Software Quality Analyst. Toan Huynh received a B.Sc. in Computer Engineering from the University of Alberta, Canada. He is currently a PhD candidate at the same institution. His research interests include: web systems, e-commerce, software testing, vulnerabilities and defect management, and software approaches to the production of secure systems. Marek Reformat received his M.Sc. degree from Technical University of Poznan, Poland, and his Ph.D. from University of Manitoba, Canada. His interests were related to simulation and modeling in time-domain, as well as evolutionary computing and its application to optimization problems. For three years he worked for the Manitoba HVDC Research Centre, Canada, where he was a member of a simulation software development team. Currently, Marek Reformat is with the Department of Electrical and Computer Engineering at University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, as well as probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. Dr. Reformat has been a member of program committees of several conferences related to Computational Intelligence and evolutionary computing. He is a member of the IEEE Computer Society and ACM. James Miller received the B.Sc. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship, and subsequently a senior lectureship in Computer Science. Initially during this period his research interests were in Computer Vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests have been in Software and Systems Engineering. In 2000, he joined the Department of Electrical and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate software verification and validation issues across various domains, including embedded, web-based and ubiquitous environments. He has published over one hundred refereed journal and conference papers on Software and Systems Engineering (see www.steam.ualberta.ca for details on recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献
James MillerEmail: |
Ping Li received her M.Sc. in Computer Engineering from the University of Alberta, Canada, in 2004. She is currently working for Waterloo Hydrogeologic Inc., a Schlumberger Company, as a Software Quality Analyst. Toan Huynh received a B.Sc. in Computer Engineering from the University of Alberta, Canada. He is currently a PhD candidate at the same institution. His research interests include: web systems, e-commerce, software testing, vulnerabilities and defect management, and software approaches to the production of secure systems. Marek Reformat received his M.Sc. degree from Technical University of Poznan, Poland, and his Ph.D. from University of Manitoba, Canada. His interests were related to simulation and modeling in time-domain, as well as evolutionary computing and its application to optimization problems. For three years he worked for the Manitoba HVDC Research Centre, Canada, where he was a member of a simulation software development team. Currently, Marek Reformat is with the Department of Electrical and Computer Engineering at University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, as well as probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. Dr. Reformat has been a member of program committees of several conferences related to Computational Intelligence and evolutionary computing. He is a member of the IEEE Computer Society and ACM. James Miller received the B.Sc. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship, and subsequently a senior lectureship in Computer Science. Initially during this period his research interests were in Computer Vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests have been in Software and Systems Engineering. In 2000, he joined the Department of Electrical and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate software verification and validation issues across various domains, including embedded, web-based and ubiquitous environments. He has published over one hundred refereed journal and conference papers on Software and Systems Engineering (see www.steam.ualberta.ca for details on recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献
10.
Periodicity and stability issues of a <Emphasis Type="Italic">chaotic</Emphasis> pattern recognition neural network 总被引:1,自引:1,他引:0
Traditional pattern recognition (PR) systems work with the model that the object to be recognized is characterized by a set
of features, which are treated as the inputs. In this paper, we propose a new model for PR, namely one that involves chaotic
neural networks (CNNs). To achieve this, we enhance the basic model proposed by Adachi (Neural Netw 10:83–98, 1997), referred to as Adachi’s Neural Network (AdNN), which though dynamic, is not chaotic. We demonstrate that by decreasing the multiplicity of the eigenvalues of the
AdNN’s control system, we can effectively drive the system into chaos. We prove this result here by eigenvalue computations
and the evaluation of the Lyapunov exponent. With this premise, we then show that such a Modified AdNN (M-AdNN) has the desirable
property that it recognizes various input patterns. The way that this PR is achieved is by the system essentially sympathetically “resonating” with a finite periodicity whenever these samples (or their reasonable resemblances) are presented. In this paper, we analyze the M-AdNN for its periodicity, stability
and the length of the transient phase of the retrieval process. The M-AdNN has been tested for Adachi’s dataset and for a
real-life PR problem involving numerals. We believe that this research also opens a host of new research avenues.
Research partially supported by the Natural Sciences and Engineering Research Council of Canada.
Dragos Calitoiu was born in Iasi, Romania on May 7, 1968. He obtained his Electronics degree in 1993 from the Polytechnical University of Bucharest, Romania, and the Ph. D. degree in 2006, from Carleton University, in Ottawa, Canada. He is currently a Postdoctoral Fellow with the Health Policy Research Division of Health Canada. His research interests include Pattern Recognition, Machine Learning, Learning Automata, Chaos Theory and Computational Neuroscience. B. John Oommen was born in Coonoor, India on September 9, 1953. He obtained his B. Tech. degree from the Indian Institute of Technology, Madras, India in 1975. He obtained his M. E. from the Indian Institute of Science in Bangalore, India in 1977. He then went on for his M. S. and Ph. D. which he obtained from Purdue University, in West Lafayettte, Indiana in 1979 and 1982, respectively. He joined the School of Computer Science at Carleton University in Ottawa, Canada, in the 1981–1982 academic year. He is still at Carleton and holds the rank of a Full Professor. His research interests include Automata Learning, Adaptive Data Structures, Statistical and Syntactic Pattern Recognition, Stochastic Algorithms and Partitioning Algorithms. He is the author of more than 260 refereed journal and conference publications and is a Fellow of the IEEE and a Fellow of the IAPR. Dr. Oommen is on the Editorial Board of the IEEE Transactions on Systems, Man and Cybernetics, and Pattern Recognition. Doron Nussbaum received his B.Sc. degree in mathematics and computer science from the University of Tel-Aviv, Israel in 1985, and the M. C. S. and Ph. D. degrees in computer science from Carleton University, Ottawa, Canada in 1988 and 2001, respectively. From 1988 to 1991 he worked for Tydac Technologies as a Manager of Research and Development. His work at Tydac focused on the development of a geographical information system. From 1991 to 1994, he worked for Theratronics as senior software consultant where he worked on the company’s cancer treatment planning software (Theraplan). From 1998 to 2001 he worked for SHL Systemshouse as a senior technical architect. In 2001 he joined the School of Computer Science at Carleton University as an Associate Professor. Dr. Nussbaum’s main research interests are medical computing, computational geometry, robotics and algorithms design. 相似文献
Dragos Calitoiu (Corresponding author)Email: |
B. John OommenEmail: |
Doron NussbaumEmail: |
Dragos Calitoiu was born in Iasi, Romania on May 7, 1968. He obtained his Electronics degree in 1993 from the Polytechnical University of Bucharest, Romania, and the Ph. D. degree in 2006, from Carleton University, in Ottawa, Canada. He is currently a Postdoctoral Fellow with the Health Policy Research Division of Health Canada. His research interests include Pattern Recognition, Machine Learning, Learning Automata, Chaos Theory and Computational Neuroscience. B. John Oommen was born in Coonoor, India on September 9, 1953. He obtained his B. Tech. degree from the Indian Institute of Technology, Madras, India in 1975. He obtained his M. E. from the Indian Institute of Science in Bangalore, India in 1977. He then went on for his M. S. and Ph. D. which he obtained from Purdue University, in West Lafayettte, Indiana in 1979 and 1982, respectively. He joined the School of Computer Science at Carleton University in Ottawa, Canada, in the 1981–1982 academic year. He is still at Carleton and holds the rank of a Full Professor. His research interests include Automata Learning, Adaptive Data Structures, Statistical and Syntactic Pattern Recognition, Stochastic Algorithms and Partitioning Algorithms. He is the author of more than 260 refereed journal and conference publications and is a Fellow of the IEEE and a Fellow of the IAPR. Dr. Oommen is on the Editorial Board of the IEEE Transactions on Systems, Man and Cybernetics, and Pattern Recognition. Doron Nussbaum received his B.Sc. degree in mathematics and computer science from the University of Tel-Aviv, Israel in 1985, and the M. C. S. and Ph. D. degrees in computer science from Carleton University, Ottawa, Canada in 1988 and 2001, respectively. From 1988 to 1991 he worked for Tydac Technologies as a Manager of Research and Development. His work at Tydac focused on the development of a geographical information system. From 1991 to 1994, he worked for Theratronics as senior software consultant where he worked on the company’s cancer treatment planning software (Theraplan). From 1998 to 2001 he worked for SHL Systemshouse as a senior technical architect. In 2001 he joined the School of Computer Science at Carleton University as an Associate Professor. Dr. Nussbaum’s main research interests are medical computing, computational geometry, robotics and algorithms design. 相似文献
11.
Antonio Ciampi Yves Lechevallier Manuel Castejón Limas Ana González Marcos 《Pattern Analysis & Applications》2008,11(2):199-220
The problem of clustering subpopulations on the basis of samples is considered within a statistical framework: a distribution
for the variables is assumed for each subpopulation and the dissimilarity between any two populations is defined as the likelihood
ratio statistic which compares the hypothesis that the two subpopulations differ in the parameter of their distributions to
the hypothesis that they do not. A general algorithm for the construction of a hierarchical classification is described which
has the important property of not having inversions in the dendrogram. The essential elements of the algorithm are specified
for the case of well-known distributions (normal, multinomial and Poisson) and an outline of the general parametric case is
also discussed. Several applications are discussed, the main one being a novel approach to dealing with massive data in the
context of a two-step approach. After clustering the data in a reasonable number of ‘bins’ by a fast algorithm such as k-Means, we apply a version of our algorithm to the resulting bins. Multivariate normality for the means calculated on each
bin is assumed: this is justified by the central limit theorem and the assumption that each bin contains a large number of
units, an assumption generally justified when dealing with truly massive data such as currently found in modern data analysis.
However, no assumption is made about the data generating distribution.
Antonio Ciampi received his M.Sc. and Ph.D. degrees from Queen's University, Kingston, Ontario, Canada in 1973. He taught at the University of Zambia from 1973 to 1977. Returning to Canada he worked as statitician in the Treasury of the Ontario Government. From 1978 to 1985, he was Senior Scientist in the Ontario Cancer Institute, Toronto, and taught at the University of Toronto. In 1985 he moved to Montreal where he is Associate Professor in the Department of Epidemiology, Biostatistics and Occupational Health, McGill University. He has also been Senior Scientist of the Montreal Children's Hospital Research Instititue, in the Montreal Heart Institute and in the St. Mary's Hospital Community Health Research Unit. His research interest include Statistical Learning, Data Mining and Statistical Modeling. Yves Lechevallier In 1976 he joined the INRIA where he was engaged in the project of Clustering and Pattern Recognition. Since 1988 he has been teaching Clustering, Neural Network and Data Mining at the University of PARIS-IX, CNAM and ENSAE. He specializes in Mathematical Statistics, Applied Statistics, Data Analysis and Classification. Current Research Interests: (1) Clustering algorithm (Dynamic Clustering Method, Kohonen Maps, Divisive Clustering Method); (2) Discrimination Problems and Decision Tree Methods; Build an efficient Neural Network by Classification Tree. Manuel Castejón Limas received his engineering degree from the Universidad de Oviedo in 1999 and his Ph.D. degree from the Universidad de La Rioja in 2004. From 2002 he teaches project management at the Universidad de Leon. His research is oriented towards the development of data analysis procedures that may aid project managers on their decision making processes. Ana González Marcos received her M.Sc. and Ph.D. degrees from the University of La Rioja, Spain. In 2003, she joined the University of León, Spain, where she works as a Lecturer in the Department of Mechanical, Informatic and Aerospace Engineering. Her research interests include the application of multivariate analysis and artificial intelligence techniques in order to improve the quality of industrial processes. 相似文献
Antonio CiampiEmail: |
Antonio Ciampi received his M.Sc. and Ph.D. degrees from Queen's University, Kingston, Ontario, Canada in 1973. He taught at the University of Zambia from 1973 to 1977. Returning to Canada he worked as statitician in the Treasury of the Ontario Government. From 1978 to 1985, he was Senior Scientist in the Ontario Cancer Institute, Toronto, and taught at the University of Toronto. In 1985 he moved to Montreal where he is Associate Professor in the Department of Epidemiology, Biostatistics and Occupational Health, McGill University. He has also been Senior Scientist of the Montreal Children's Hospital Research Instititue, in the Montreal Heart Institute and in the St. Mary's Hospital Community Health Research Unit. His research interest include Statistical Learning, Data Mining and Statistical Modeling. Yves Lechevallier In 1976 he joined the INRIA where he was engaged in the project of Clustering and Pattern Recognition. Since 1988 he has been teaching Clustering, Neural Network and Data Mining at the University of PARIS-IX, CNAM and ENSAE. He specializes in Mathematical Statistics, Applied Statistics, Data Analysis and Classification. Current Research Interests: (1) Clustering algorithm (Dynamic Clustering Method, Kohonen Maps, Divisive Clustering Method); (2) Discrimination Problems and Decision Tree Methods; Build an efficient Neural Network by Classification Tree. Manuel Castejón Limas received his engineering degree from the Universidad de Oviedo in 1999 and his Ph.D. degree from the Universidad de La Rioja in 2004. From 2002 he teaches project management at the Universidad de Leon. His research is oriented towards the development of data analysis procedures that may aid project managers on their decision making processes. Ana González Marcos received her M.Sc. and Ph.D. degrees from the University of La Rioja, Spain. In 2003, she joined the University of León, Spain, where she works as a Lecturer in the Department of Mechanical, Informatic and Aerospace Engineering. Her research interests include the application of multivariate analysis and artificial intelligence techniques in order to improve the quality of industrial processes. 相似文献
12.
Empirical evaluation of optimization algorithms when used in goal-oriented automated test data generation techniques 总被引:2,自引:0,他引:2
Man Xiao Mohamed El-Attar Marek Reformat James Miller 《Empirical Software Engineering》2007,12(2):183-239
Software testing is an essential process in software development. Software testing is very costly, often consuming half the
financial resources assigned to a project. The most laborious part of software testing is the generation of test-data. Currently,
this process is principally a manual process. Hence, the automation of test-data generation can significantly cut the total
cost of software testing and the software development cycle in general. A number of automated test-data generation approaches
have already been explored. This paper highlights the goal-oriented approach as a promising approach to devise automated test-data
generators. A range of optimization techniques can be used within these goal-oriented test-data generators, and their respective
characteristics, when applied to these situations remain relatively unexplored. Therefore, in this paper, a comparative study
about the effectiveness of the most commonly used optimization techniques is conducted.
Man Xiao received a B.S. degree in Space Physics and Electronics Information Engineering from the University of Wuhan, China; and a M.S. degree in Software Engineering, from the University of Alberta, Canada. She is now a Software Engineer at a small start-up company in Edmonton, Alberta, Canada. Mohamed El-Attar is a Ph.D. candidate (Software Engineering) at the University of Alberta and a member of the STEAM laboratory. His research interests include Requirements Engineering, in particular with UML and use cases, object-oriented analysis and design, model transformation and empirical studies. Mohamed received a B.S. Engineering in Computer Systems from Carleton University. Marek Reformat received his M.S. degree from the Technical University of Poznan, Poland, and his Ph.D. from the University of Manitoba, Canada. His interests are related to simulation and modeling in time-domain, and evolutionary computing and its application to optimization problems. For 3 years he worked for the Manitoba HVDC Research Centre, Canada where he was a member of a simulation software development team. Currently, he is with the Department of Electrical and Computer Engineering at the University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, and probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. He was a member of program committees of several conferences related to computational intelligence and evolutionary computing. James Miller received his B.S. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship and subsequently a senior lectureship in Computer Science. Initially, during this period, his research interests were in computer vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests were in software and systems engineering. In 2000, he joined the Department of Electronic and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate verification and validation issues of software, embedded and ubiquitous computer systems. He has published over one hundred refereed journal and conference papers on software and systems engineering (see for details for recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献
James Miller (Corresponding author)Email: |
Man Xiao received a B.S. degree in Space Physics and Electronics Information Engineering from the University of Wuhan, China; and a M.S. degree in Software Engineering, from the University of Alberta, Canada. She is now a Software Engineer at a small start-up company in Edmonton, Alberta, Canada. Mohamed El-Attar is a Ph.D. candidate (Software Engineering) at the University of Alberta and a member of the STEAM laboratory. His research interests include Requirements Engineering, in particular with UML and use cases, object-oriented analysis and design, model transformation and empirical studies. Mohamed received a B.S. Engineering in Computer Systems from Carleton University. Marek Reformat received his M.S. degree from the Technical University of Poznan, Poland, and his Ph.D. from the University of Manitoba, Canada. His interests are related to simulation and modeling in time-domain, and evolutionary computing and its application to optimization problems. For 3 years he worked for the Manitoba HVDC Research Centre, Canada where he was a member of a simulation software development team. Currently, he is with the Department of Electrical and Computer Engineering at the University of Alberta. His research interests lay in the areas of application of Computational Intelligence techniques, such as neuro-fuzzy systems and evolutionary computing, and probabilistic and evidence theories to intelligent data analysis leading to translating data into knowledge. He applies these methods to conduct research in the areas of Software Engineering, Software Quality in particular, and Knowledge Engineering. He was a member of program committees of several conferences related to computational intelligence and evolutionary computing. James Miller received his B.S. and Ph.D. degrees in Computer Science from the University of Strathclyde, Scotland. During this period, he worked on the ESPRIT project GENEDIS on the production of a real-time stereovision system. Subsequently, he worked at the United Kingdom’s National Electronic Research Initiative on Pattern Recognition as a Principal Scientist, before returning to the University of Strathclyde to accept a lectureship and subsequently a senior lectureship in Computer Science. Initially, during this period, his research interests were in computer vision, and he was a co-investigator on the ESPRIT 2 project VIDIMUS. Since 1993, his research interests were in software and systems engineering. In 2000, he joined the Department of Electronic and Computer Engineering at the University of Alberta as a full professor and in 2003 became an adjunct professor at the Department of Electrical and Computer Engineering at the University of Calgary. He is the principal investigator in a number of research projects that investigate verification and validation issues of software, embedded and ubiquitous computer systems. He has published over one hundred refereed journal and conference papers on software and systems engineering (see for details for recent directions); and currently serves on the program committee for the IEEE International Symposium on Empirical Software Engineering and Measurement; and sits on the editorial board of the Journal of Empirical Software Engineering. 相似文献
13.
In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the
outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure
for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications.
In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data.
Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure
deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed
objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure
in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian
Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs
consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity
of a shape.
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献
Francesco de PasqualeEmail: |
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献
14.
Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. It is a progressive disease
where the early stages are characterised by a diffuse appearance of small air spaces, and later stages exhibit large air spaces
called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, it is shown that an automated
texture-based system based on co-training is capable of achieving multiple levels of emphysema extraction in high-resolution
computed tomography (HRCT) images. Co-training is a semi-supervised technique used to improve classifiers that are trained
with very few labelled examples using a large pool of unseen examples over two disjoint feature sets called views. It is also
shown that examples labelled by experts can be incorporated within the system in an incremental manner. The results are also
compared against “density mask”, currently a standard approach used for emphysema detection in medical image analysis and
other computerized techniques used for classification of emphysema in the literature. The new system can classify diffuse
regions of emphysema starting from a bullous setting. The classifiers built at different iterations also appear to show an
interesting correlation with different levels of emphysema, which deserves more exploration.
Mithun Prasad received his PhD from the University of New South Wales, Sydney, Australia in 2006. He was a postdoctoral scholar at the University of California, Los Angeles and now a research associate at Rensselaer Polytechnic Institute, NY. His research interests are computer aided diagnosis, cell and tissue image analysis. Arcot Sowmya is a Professor, School of Computer Science and Engineering, UNSW, Sydney. She holds a PhD degree in Computer Science from Indian Institute of Technology, Bombay, besides other degrees in Mathematics and Computer Science. Her areas of research include learning in vision as well as embedded system design. Her research has been applied to extraction of linear features in remotely sensed images as well as feature extraction, recognition and computer aided diagnosis in medical images. Peter Wilson is a clinical Radiologist at Pittwater Radiology in Sydney. He was trained at Royal North Shore Hospital and taught Body Imaging at the University of Rochester, NY, prior to taking up his current position. 相似文献
Mithun Prasad (Corresponding author)Email: |
Arcot SowmyaEmail: |
Peter WilsonEmail: |
Mithun Prasad received his PhD from the University of New South Wales, Sydney, Australia in 2006. He was a postdoctoral scholar at the University of California, Los Angeles and now a research associate at Rensselaer Polytechnic Institute, NY. His research interests are computer aided diagnosis, cell and tissue image analysis. Arcot Sowmya is a Professor, School of Computer Science and Engineering, UNSW, Sydney. She holds a PhD degree in Computer Science from Indian Institute of Technology, Bombay, besides other degrees in Mathematics and Computer Science. Her areas of research include learning in vision as well as embedded system design. Her research has been applied to extraction of linear features in remotely sensed images as well as feature extraction, recognition and computer aided diagnosis in medical images. Peter Wilson is a clinical Radiologist at Pittwater Radiology in Sydney. He was trained at Royal North Shore Hospital and taught Body Imaging at the University of Rochester, NY, prior to taking up his current position. 相似文献
15.
In this paper, new measures—called clustering performance measures (CPMs)—for assessing the reliability of a clustering algorithm
are proposed. These CPMs are defined using a validation measure, which determines how well the algorithm works with a given set of parameter values, and a repeatability measure, which is used for studying the stability of the clustering solutions and has the ability to estimate the correct number
of clusters in a dataset. These proposed CPMs can be used to evaluate clustering algorithms that have a structure bias to
certain types of data distribution as well as those that have no structure biases. Additionally, we propose a novel cluster
validity index, V
I
index, which is able to handle non-spherical clusters. Five clustering algorithms on different types of real-world data and
synthetic data are evaluated. The first dataset type refers to a communications signal dataset representing one modulation
scheme under a variety of noise conditions, the second represents two breast cancer datasets, while the third type represents
different synthetic datasets with arbitrarily shaped clusters. Additionally, comparisons with other methods for estimating
the number of clusters indicate the applicability and reliability of the proposed cluster validity
V
I
index and repeatability measure for correct estimation of the number of clusters.
Sameh A. Salem graduated with a BSc degree in Communications and Electronics Engineering and an MSc in Communications and Electronics Engineering, both from Helwan University, Cairo, Egypt, in May 1998 and October 2003, respectively. He is currently pursuing PhD degree in the Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, UK. His research interests include clustering algorithms, machine learning, and parallel computing. Asoke K. Nandi received PhD degree from the University of Cambridge (Trinity College), Cambridge, UK, in 1979. He held several research positions in Rutherford Appleton Laboratory (UK), European Organisation for Nuclear Research (Switzerland), Department of Physics, Queen Mary College (London, UK) and Department of Nuclear Physics (Oxford, UK). In 1987, he joined the Imperial College, London, UK, as the Solartron Lecturer in the Signal Processing Section of the Electrical Engineering Department. In 1991, he joined the Signal Processing Division of the Electronic and Electrical Engineering Department in the University of Strathclyde, Glasgow, UK, as a Senior Lecturer; subsequently, he was appointed as a Reader in 1995 and a Professor in 1998. In March 1999, he moved to the University of Liverpool, Liverpool, UK to take up his appointment with David Jardine, Chair of Signal Processing in the Department of Electrical Engineering and Electronics. In 1983, he was a member of the UA1 team at CERN that discovered the three fundamental particles known as W+, W− and Z0 providing the evidence for the unification of the electromagnetic and weak forces, which was recognised by the Nobel Committee for Physics in 1984. Currently, he is the Head of the Signal Processing and Communications Research Group with interests in the areas of non-Gaussian signal processing, communications, and machine learning research. With his group he has been carrying out research in machine condition monitoring, signal modelling, system identification, communication signal processing, biomedical signals, ultrasonics, blind source separation, and blind deconvolution. He has authored or co-authored over 350 technical publications, including two books “Automatic Modulation Recognition of Communications Signals” (Kluwer Academic, Boston, MA, 1996) and “Blind Estimation Using Higher-Order Statistics” (Kluwer Academic, Boston, MA, 1999) and over 140 journal papers. Professor Nandi was awarded the Mounbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers of the UK in 1998 and the Water Arbitration Prize of the Institution of Mechanical Engineers of the UK in 1999. He is a Fellow of the Cambridge Philosophical Society, the Institution of Engineering and Technology, the Institute of Mathematics and its applications, the Institute of Physics, the Royal Society for Arts, the Institution of Mechanical Engineers, and the British Computer Society. 相似文献
Asoke K. NandiEmail: |
Sameh A. Salem graduated with a BSc degree in Communications and Electronics Engineering and an MSc in Communications and Electronics Engineering, both from Helwan University, Cairo, Egypt, in May 1998 and October 2003, respectively. He is currently pursuing PhD degree in the Signal Processing and Communications Group, Department of Electrical Engineering and Electronics, The University of Liverpool, UK. His research interests include clustering algorithms, machine learning, and parallel computing. Asoke K. Nandi received PhD degree from the University of Cambridge (Trinity College), Cambridge, UK, in 1979. He held several research positions in Rutherford Appleton Laboratory (UK), European Organisation for Nuclear Research (Switzerland), Department of Physics, Queen Mary College (London, UK) and Department of Nuclear Physics (Oxford, UK). In 1987, he joined the Imperial College, London, UK, as the Solartron Lecturer in the Signal Processing Section of the Electrical Engineering Department. In 1991, he joined the Signal Processing Division of the Electronic and Electrical Engineering Department in the University of Strathclyde, Glasgow, UK, as a Senior Lecturer; subsequently, he was appointed as a Reader in 1995 and a Professor in 1998. In March 1999, he moved to the University of Liverpool, Liverpool, UK to take up his appointment with David Jardine, Chair of Signal Processing in the Department of Electrical Engineering and Electronics. In 1983, he was a member of the UA1 team at CERN that discovered the three fundamental particles known as W+, W− and Z0 providing the evidence for the unification of the electromagnetic and weak forces, which was recognised by the Nobel Committee for Physics in 1984. Currently, he is the Head of the Signal Processing and Communications Research Group with interests in the areas of non-Gaussian signal processing, communications, and machine learning research. With his group he has been carrying out research in machine condition monitoring, signal modelling, system identification, communication signal processing, biomedical signals, ultrasonics, blind source separation, and blind deconvolution. He has authored or co-authored over 350 technical publications, including two books “Automatic Modulation Recognition of Communications Signals” (Kluwer Academic, Boston, MA, 1996) and “Blind Estimation Using Higher-Order Statistics” (Kluwer Academic, Boston, MA, 1999) and over 140 journal papers. Professor Nandi was awarded the Mounbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers of the UK in 1998 and the Water Arbitration Prize of the Institution of Mechanical Engineers of the UK in 1999. He is a Fellow of the Cambridge Philosophical Society, the Institution of Engineering and Technology, the Institute of Mathematics and its applications, the Institute of Physics, the Royal Society for Arts, the Institution of Mechanical Engineers, and the British Computer Society. 相似文献
16.
Edwin K. P. Chong Christopher M. Kreucher Alfred O. Hero III 《Discrete Event Dynamic Systems》2009,19(3):377-422
Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification,
and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach
to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the
problem. Such approximations are necessary because of the very large state space involved in practical adaptive sensing problems,
precluding exact computation of optimal solutions. We review the theory of POMDPs and show how the theory applies to adaptive
sensing problems. We then describe a variety of approximation methods, with examples to illustrate their application in adaptive
sensing. The examples also demonstrate the gains that are possible from nonmyopic methods relative to myopic methods, and
highlight some insights into the dependence of such gains on the sensing resources and environment.
Edwin K. P. Chong received the BE(Hons) degree with First Class Honors from the University of Adelaide, South Australia, in 1987; and the MA and PhD degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991, where he was named a University Faculty Scholar in 1999, and was promoted to Professor in 2001. Since August 2001, he has been a Professor of Electrical and Computer Engineering and a Professor of Mathematics at Colorado State University. His research interests span the areas of communication and sensor networks, stochastic modeling and control, and optimization methods. He coauthored the recent best-selling book, An Introduction to Optimization, 3rd Edition, Wiley-Interscience, 2008. He is currently on the editorial board of the IEEE Transactions on Automatic Control, Computer Networks, Journal of Control Science and Engineering, and IEEE Expert Now. He is a Fellow of the IEEE, and served as an IEEE Control Systems Society Distinguished Lecturer. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. He has served as Principal Investigator for numerous funded projects from NSF, DARPA, and other funding agencies. Christopher M. Kreucher received the BS, MS, and PhD degrees in Electrical Engineering from the University of Michigan in 1997, 1998, and 2005, respectively. He is currently a Senior Systems Engineer at Integrity Applications Incorporated in Ann Arbor, Michigan. His current research interests include nonlinear filtering (specifically particle filtering), Bayesian methods of fusion and multitarget tracking, self localization, information theoretic sensor management, and distributed swarm management. Alfred O. Hero III received the BS (summa cum laude) from Boston University (1980) and the PhD from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is a Professor in the Department of Electrical Engineering and Computer Science and, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He has held visiting positions at Massachusetts Institute of Technology (2006), Boston University, I3S University of Nice, Sophia-Antipolis, France (2001), Ecole Normale Superieure de Lyon (1999), Ecole Nationale Superieure des Telecommunications, Paris (1999), Scientific Research Labs of the Ford Motor Company, Dearborn, Michigan (1993), Ecole Nationale Superieure des Techniques Avancees (ENSTA), Ecole Superieure d’Electricite, Paris (1990), and M.I.T. Lincoln Laboratory (1987–1989). His recent research interests have been in areas including: inference for sensor networks, adaptive sensing, bioinformatics, inverse problems. and statistical signal and image processing. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of Tau Beta Pi, the American Statistical Association (ASA), the Society for Industrial and Applied Mathematics (SIAM), and the US National Commission (Commission C) of the International Union of Radio Science (URSI). He has received a IEEE Signal Processing Society Meritorious Service Award (1998), IEEE Signal Processing Society Best Paper Award (1998), a IEEE Third Millenium Medal and a 2002 IEEE Signal Processing Society Distinguished Lecturership. He was President of the IEEE Signal Processing Society (2006–2007) and during his term served on the TAB Periodicals Committee (2006). He was a member of the IEEE TAB Society Review Committee (2008) and is Director-elect of IEEE for Division IX (2009). 相似文献
Alfred O. Hero IIIEmail: |
Edwin K. P. Chong received the BE(Hons) degree with First Class Honors from the University of Adelaide, South Australia, in 1987; and the MA and PhD degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991, where he was named a University Faculty Scholar in 1999, and was promoted to Professor in 2001. Since August 2001, he has been a Professor of Electrical and Computer Engineering and a Professor of Mathematics at Colorado State University. His research interests span the areas of communication and sensor networks, stochastic modeling and control, and optimization methods. He coauthored the recent best-selling book, An Introduction to Optimization, 3rd Edition, Wiley-Interscience, 2008. He is currently on the editorial board of the IEEE Transactions on Automatic Control, Computer Networks, Journal of Control Science and Engineering, and IEEE Expert Now. He is a Fellow of the IEEE, and served as an IEEE Control Systems Society Distinguished Lecturer. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. He has served as Principal Investigator for numerous funded projects from NSF, DARPA, and other funding agencies. Christopher M. Kreucher received the BS, MS, and PhD degrees in Electrical Engineering from the University of Michigan in 1997, 1998, and 2005, respectively. He is currently a Senior Systems Engineer at Integrity Applications Incorporated in Ann Arbor, Michigan. His current research interests include nonlinear filtering (specifically particle filtering), Bayesian methods of fusion and multitarget tracking, self localization, information theoretic sensor management, and distributed swarm management. Alfred O. Hero III received the BS (summa cum laude) from Boston University (1980) and the PhD from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is a Professor in the Department of Electrical Engineering and Computer Science and, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He has held visiting positions at Massachusetts Institute of Technology (2006), Boston University, I3S University of Nice, Sophia-Antipolis, France (2001), Ecole Normale Superieure de Lyon (1999), Ecole Nationale Superieure des Telecommunications, Paris (1999), Scientific Research Labs of the Ford Motor Company, Dearborn, Michigan (1993), Ecole Nationale Superieure des Techniques Avancees (ENSTA), Ecole Superieure d’Electricite, Paris (1990), and M.I.T. Lincoln Laboratory (1987–1989). His recent research interests have been in areas including: inference for sensor networks, adaptive sensing, bioinformatics, inverse problems. and statistical signal and image processing. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a member of Tau Beta Pi, the American Statistical Association (ASA), the Society for Industrial and Applied Mathematics (SIAM), and the US National Commission (Commission C) of the International Union of Radio Science (URSI). He has received a IEEE Signal Processing Society Meritorious Service Award (1998), IEEE Signal Processing Society Best Paper Award (1998), a IEEE Third Millenium Medal and a 2002 IEEE Signal Processing Society Distinguished Lecturership. He was President of the IEEE Signal Processing Society (2006–2007) and during his term served on the TAB Periodicals Committee (2006). He was a member of the IEEE TAB Society Review Committee (2008) and is Director-elect of IEEE for Division IX (2009). 相似文献
17.
We present a probabilistic multiple cause model for the analysis of binary (0–1) data. A distinctive feature of the aspect
Bernoulli (AB) model is its ability to automatically detect and distinguish between “true absences” and “false absences” (both
of which are coded as 0 in the data), and similarly, between “true presences” and “false presences” (both of which are coded
as 1). This is accomplished by specific additive noise components which explicitly account for such non-content bearing causes.
The AB model is thus suitable for noise removal and data explanatory purposes, including omission/addition detection. An important
application of AB that we demonstrate is data-driven reasoning about palaeontological recordings. Additionally, results on
recovering corrupted handwritten digit images and expanding short text documents are also given, and comparisons to other
methods are demonstrated and discussed.
Ella Bingham received her M.Sc. degree in Engineering Physics and Mathematics at Helsinki University of Technology in 1998, and her Dr.Sc. degree in Computer Science at Helsinki University of Technology in 2003. She is currently at Helsinki Institute for Information Technology, located at the University of Helsinki. Her research interests include statistical data analysis and machine learning. Ata Kabán is a lecturer in the School of Computer Science of the University of Birmingham, since 2003. She holds a B.Sc. degree in computer science (1999) from the University “Babes-Bolya” of Cluj-Napoca, Romania, and a Ph.D. in computer science (2001) from the University of Paisley, UK. Her current research interests concern statistical machine learning and data mining. Prior to her career in computer science, she obtained a B.A. degree in musical composition (1994) and the M.A. (1995) and Ph.D. (1999) degrees in musicology from the Music Academy “Gh. Dima” of Cluj-Napoca, Romania. Mikael Fortelius is a palaeontologist with special interest in plant-eating mammals of the Cenozoic, especially ungulates and their relationship with habitat and climate change (the Ungulate Condition). Mikael is Professor of Evolutionary Palaeontology in the Department of Geology and Group Leader in the Institute of Biotechnology (BI), University of Helsinki. Since 1992, he has been engaged in developing a database of Neogene Old World Mammals (). The NOW database is maintained at the Finnish Museum of Natural History and developed in collaboration with an extensive Advisory Board; data access and downloading are entirely public. 相似文献
Mikael ForteliusEmail: |
Ella Bingham received her M.Sc. degree in Engineering Physics and Mathematics at Helsinki University of Technology in 1998, and her Dr.Sc. degree in Computer Science at Helsinki University of Technology in 2003. She is currently at Helsinki Institute for Information Technology, located at the University of Helsinki. Her research interests include statistical data analysis and machine learning. Ata Kabán is a lecturer in the School of Computer Science of the University of Birmingham, since 2003. She holds a B.Sc. degree in computer science (1999) from the University “Babes-Bolya” of Cluj-Napoca, Romania, and a Ph.D. in computer science (2001) from the University of Paisley, UK. Her current research interests concern statistical machine learning and data mining. Prior to her career in computer science, she obtained a B.A. degree in musical composition (1994) and the M.A. (1995) and Ph.D. (1999) degrees in musicology from the Music Academy “Gh. Dima” of Cluj-Napoca, Romania. Mikael Fortelius is a palaeontologist with special interest in plant-eating mammals of the Cenozoic, especially ungulates and their relationship with habitat and climate change (the Ungulate Condition). Mikael is Professor of Evolutionary Palaeontology in the Department of Geology and Group Leader in the Institute of Biotechnology (BI), University of Helsinki. Since 1992, he has been engaged in developing a database of Neogene Old World Mammals (). The NOW database is maintained at the Finnish Museum of Natural History and developed in collaboration with an extensive Advisory Board; data access and downloading are entirely public. 相似文献
18.
Fault based testing aims at detecting hypothesized faults based on specifications or program source. There are some fault
based techniques for testing Boolean expressions which are commonly used to model conditions in specifications as well as
logical decisions in program source. The MUMCUT strategy has been proposed to generate test cases from Boolean expressions.
Moreover, it detects eight common types of hypothesized faults provided that the original expression is in irredundant disjunctive
normal form, IDNF. Software practitioners are more likely to write the conditions and logical decisions in general form rather
than IDNF. Hence, it is interesting to investigate the fault detecting capability of the MUMCUT strategy with respect to general
form Boolean expressions. In this article, we perform empirical studies to investigate the fault detection capability of the
MUMCUT strategy with respect to general form Boolean expressions as well as mutated expressions. A mutated expression can
be obtained from the original given Boolean expression by making a syntactic change based on a particular type of fault.
T. Y. Chen obtained his BSc and MPhil from the University of Hong Kong, MSc and DIC from the Imperial College of Science and Technology, PhD from the University of Melbourne. He is currently a Professor of Software Engineering at the Swinburne University of Technology. Prior to joining Swinburne, he has taught at the University of Hong Kong and the University of Melbourne. His research interests include software testing, debugging, maintenance, and validation of requirements. M. F. Lau received the Ph.D. degree in Software Engineering from the University of Melbourne, Australia. He is currently a Senior Lecturer in the Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia. His research publications have appeared in various scholarly journals, including ACM Transactions on Software Engineering and Methodology, The Journal of Systems and Software, The Computer Journal, Software Testing, Verification and Reliability, Information and Software Technology, Information Sciences, and Information Processing Letters. His research interests include software testing, software quality, software specification and computers in education. K. Y. Sim received his Bachelor of Engineering in Electrical, Electronics and Systems from the National University of Malaysia in 1999 and the Master of Computer Science from the University of Malaya, Malaysia in 2001. Currently, he is a Senior Lecturer in the School of Engineering, Swinburne University of Technology, Sarawak Campus, Malaysia. His current research interests include software testing and information security. C. A. Sun received the PhD degree in Computer Software and Theory in 2002 from Beijing University of Aeronautics and Astronautics, China; the bachelor degree in Computer and Its application in 1997 from University of Science and Technology Beijing, China. He is currently an Assistant Professor in the School of Computer and Information Technology, Beijing Jiaotong University, China. His research areas are software testing, software architecture and service-oriented computing. He has published about 40 referred papers in the above areas. He is an IEEE member. 相似文献
M. F. LauEmail: |
T. Y. Chen obtained his BSc and MPhil from the University of Hong Kong, MSc and DIC from the Imperial College of Science and Technology, PhD from the University of Melbourne. He is currently a Professor of Software Engineering at the Swinburne University of Technology. Prior to joining Swinburne, he has taught at the University of Hong Kong and the University of Melbourne. His research interests include software testing, debugging, maintenance, and validation of requirements. M. F. Lau received the Ph.D. degree in Software Engineering from the University of Melbourne, Australia. He is currently a Senior Lecturer in the Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia. His research publications have appeared in various scholarly journals, including ACM Transactions on Software Engineering and Methodology, The Journal of Systems and Software, The Computer Journal, Software Testing, Verification and Reliability, Information and Software Technology, Information Sciences, and Information Processing Letters. His research interests include software testing, software quality, software specification and computers in education. K. Y. Sim received his Bachelor of Engineering in Electrical, Electronics and Systems from the National University of Malaysia in 1999 and the Master of Computer Science from the University of Malaya, Malaysia in 2001. Currently, he is a Senior Lecturer in the School of Engineering, Swinburne University of Technology, Sarawak Campus, Malaysia. His current research interests include software testing and information security. C. A. Sun received the PhD degree in Computer Software and Theory in 2002 from Beijing University of Aeronautics and Astronautics, China; the bachelor degree in Computer and Its application in 1997 from University of Science and Technology Beijing, China. He is currently an Assistant Professor in the School of Computer and Information Technology, Beijing Jiaotong University, China. His research areas are software testing, software architecture and service-oriented computing. He has published about 40 referred papers in the above areas. He is an IEEE member. 相似文献
19.
Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method 总被引:1,自引:1,他引:0
Unlabeled training examples are readily available in many applications, but labeled examples are fairly expensive to obtain.
For instance, in our previous works on classification of peer-to-peer (P2P) Internet traffics, we observed that only about
25% of examples can be labeled as “P2P”or “NonP2P” using a port-based heuristic rule. We also expect that even fewer examples
can be labeled in the future as more and more P2P applications use dynamic ports. This fact motivates us to investigate the
techniques which enhance the accuracy of P2P traffic classification by exploiting the unlabeled examples. In addition, the
Internet data flows dynamically in large volumes (streaming data). In P2P applications, new communities of peers often join
and old communities of peers often leave, requiring the classifiers to be capable of updating the model incrementally, and
dealing with concept drift. Based on these requirements, this paper proposes an incremental Tri-Training (iTT) algorithm.
We tested our approach on a real data stream with 7.2 Mega labeled examples and 20.4 Mega unlabeled examples. The results
show that iTT algorithm can enhance accuracy of P2P traffic classification by exploiting unlabeled examples. In addition,
it can effectively deal with dynamic nature of streaming data to detect the changes in communities of peers. We extracted
attributes only from the IP layer, eliminating the privacy concern associated with the techniques that use deep packet inspection.
Bijan Raahemi is an assistant professor at the Telfer School of Management, University of Ottawa, Canada, with cross-appointment with the School of Information Technology and Engineering. He received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada, in 1997. Prior to joining the University of Ottawa, Dr. Raahemi held several research positions in Telecommunications industry, including Nortel Networks and Alcatel-Lucent, focusing on Computer Networks Architectures and Services, Dynamics of Internet Traffic, Systems Modeling, and Performance Analysis of Data Networks. His current research interests include Knowledge Discovery and Data Mining, Information Systems, and Data Communications Networks. Dr. Raahemi’s work has appeared in several peer-reviewed journals and conference proceedings. He also holds 10 patents in Data Communications. He is a senior Member of the Institute of Electrical and Electronics Engineering (IEEE), and a member of the Association for Computing Machinery (ACM). Weicai Zhong is a post-doctoral fellow at the Telfer School of Management, University of Ottawa, Canada. He received a B.S. degree in computer science and technology from Xidian University, Xi’an, China, in 2000 and a Ph.D. in pattern recognition and intelligent systems from Xidian University in 2004. Prior to joining the University of Ottawa, Dr. Zhong was a senior statistician in SPSS Inc. from Jan. 2005 to Dec. 2007. His current research interests include Internet Traffic Identification, Data Mining, and Evolutionary Computation. He is a member of the Institute of Electrical and Electronics Engineering (IEEE). Jing Liu is an Associate Professor with Xidian University, China. She received a B.S. degree in computer science and technology from Xidian University, Xi’an, China, in 2000, and a Ph.D. in circuits and systems from Xidian University in 2004. Her research interests include Data Mining, Evolutionary Computation, and Multiagent Systems. She is a member of the Institute of Electrical and Electronics Engineering (IEEE). 相似文献
Jing LiuEmail: |
Bijan Raahemi is an assistant professor at the Telfer School of Management, University of Ottawa, Canada, with cross-appointment with the School of Information Technology and Engineering. He received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada, in 1997. Prior to joining the University of Ottawa, Dr. Raahemi held several research positions in Telecommunications industry, including Nortel Networks and Alcatel-Lucent, focusing on Computer Networks Architectures and Services, Dynamics of Internet Traffic, Systems Modeling, and Performance Analysis of Data Networks. His current research interests include Knowledge Discovery and Data Mining, Information Systems, and Data Communications Networks. Dr. Raahemi’s work has appeared in several peer-reviewed journals and conference proceedings. He also holds 10 patents in Data Communications. He is a senior Member of the Institute of Electrical and Electronics Engineering (IEEE), and a member of the Association for Computing Machinery (ACM). Weicai Zhong is a post-doctoral fellow at the Telfer School of Management, University of Ottawa, Canada. He received a B.S. degree in computer science and technology from Xidian University, Xi’an, China, in 2000 and a Ph.D. in pattern recognition and intelligent systems from Xidian University in 2004. Prior to joining the University of Ottawa, Dr. Zhong was a senior statistician in SPSS Inc. from Jan. 2005 to Dec. 2007. His current research interests include Internet Traffic Identification, Data Mining, and Evolutionary Computation. He is a member of the Institute of Electrical and Electronics Engineering (IEEE). Jing Liu is an Associate Professor with Xidian University, China. She received a B.S. degree in computer science and technology from Xidian University, Xi’an, China, in 2000, and a Ph.D. in circuits and systems from Xidian University in 2004. Her research interests include Data Mining, Evolutionary Computation, and Multiagent Systems. She is a member of the Institute of Electrical and Electronics Engineering (IEEE). 相似文献
20.
Advances in GML for Geospatial Applications 总被引:1,自引:0,他引:1
Chang-Tien Lu Raimundo F. Dos SantosJr Lakshmi N. Sripada Yufeng Kou 《GeoInformatica》2007,11(1):131-157
This paper presents a study of Geography Markup Language (GML), the issues that arise from using GML for spatial applications,
including storage, parsing, querying and visualization, as well as the use of GML for mobile devices and web services. GML
is a modeling language developed by the Open Geospatial Consortium (OGC) as a medium of uniform geographic data storage and
exchange among diverse applications. Many new XML-based languages are being developed as open standards in various areas of
application. It would be beneficial to integrate such languages with GML during the developmental stages, taking full advantage
of a non-proprietary universal standard. As GML is a relatively new language still in development, data processing techniques
need to be refined further in order for GML to become a more efficient medium for geospatial applications.
Chang-Tien(C.T.) Lu received the BS degree in Computer Science and Engineering from the Tatung Institute of Technology, Taipei, Taiwan, in 1991, the MS degree in Computer Science from the Georgia Institute of Technology, Atlanta, GA, in 1996, and the Ph.D. degree in Computer Science from the University of Minnesota, Minneapolis, MN, in 2001. He is currently an assistant professor in the Department of Computer Science at Virginia Polytechnic Institute and State University, and is the founding director of the Spatial Data Management Laboratory. His research interests include spatial database, data mining, data warehousing, geographic information systems, and intelligent transportation systems. Dr. Lu is also affiliated with Virginia Tech Civil and Environmental Engineering Department, Center for Geospatial Information Technology, and Virginia Tech Transportation Institute. Raimundo Dos Santos received a Bachelor’s Degree in Computer Science from the University of South Florida. He is currently a PhD. candidate in the Department of Computer Science at Virginia Polytechnic Institute and State University. His research focuses on Spatial Data Management, including retrieval, exchange, and processing of information for Geographic Information Systems and Location-Based Services. Other interests include Geography Markup Language (GML), and data visualization. Lakshmi N Sripada received an MS in Information Systems from Virginia Polytechnic and State University in 2004. Her research interests include Data Visualization, GML, and Geographic Information Systems. Yufeng Kou received a BS degree in Computer Science from Northwestern Polytechnic University, XiAn, China, in 1996, a MS degree in Computer Science from Beijing University of Post and Telecommunications in 1999. He is a PhD candidate in Computer Science Department, Virginia Polytechnic Institute and State University. His research interests include spatial data analysis, data mining, data warehousing, and Geographic Information Systems. 相似文献
Yufeng KouEmail: |
Chang-Tien(C.T.) Lu received the BS degree in Computer Science and Engineering from the Tatung Institute of Technology, Taipei, Taiwan, in 1991, the MS degree in Computer Science from the Georgia Institute of Technology, Atlanta, GA, in 1996, and the Ph.D. degree in Computer Science from the University of Minnesota, Minneapolis, MN, in 2001. He is currently an assistant professor in the Department of Computer Science at Virginia Polytechnic Institute and State University, and is the founding director of the Spatial Data Management Laboratory. His research interests include spatial database, data mining, data warehousing, geographic information systems, and intelligent transportation systems. Dr. Lu is also affiliated with Virginia Tech Civil and Environmental Engineering Department, Center for Geospatial Information Technology, and Virginia Tech Transportation Institute. Raimundo Dos Santos received a Bachelor’s Degree in Computer Science from the University of South Florida. He is currently a PhD. candidate in the Department of Computer Science at Virginia Polytechnic Institute and State University. His research focuses on Spatial Data Management, including retrieval, exchange, and processing of information for Geographic Information Systems and Location-Based Services. Other interests include Geography Markup Language (GML), and data visualization. Lakshmi N Sripada received an MS in Information Systems from Virginia Polytechnic and State University in 2004. Her research interests include Data Visualization, GML, and Geographic Information Systems. Yufeng Kou received a BS degree in Computer Science from Northwestern Polytechnic University, XiAn, China, in 1996, a MS degree in Computer Science from Beijing University of Post and Telecommunications in 1999. He is a PhD candidate in Computer Science Department, Virginia Polytechnic Institute and State University. His research interests include spatial data analysis, data mining, data warehousing, and Geographic Information Systems. 相似文献