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1.
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
2.
Chu-Yin Chang Anthony A. Maciejewski Venkataramanan Balakrishnan Rodney G. Roberts Kishor Saitwal 《Pattern Analysis & Applications》2007,10(1):15-31
Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation,
because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require
an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes
the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection.
This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset
used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those
portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational
efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on
images of ships in a dockyard.
Chu-Yin Chang received the B.S. degree in mechanical engineering from National Central University, Chung-Li, Taiwan, ROC, in 1988, the M.S. degree in electrical engineering from the University of California, Davis, in 1993, and the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, in 1999. From 1999--2002, he was a Machine Vision Systems Engineer with Semiconductor Technologies and Instruments, Inc., Plano, TX. He is currently the Vice President of Energid Technologies, Cambridge, MA, USA. His research interests include computer vision, computer graphics, and robotics. Anthony A. Maciejewski received the BSEE, M.S., and Ph.D. degrees from Ohio State University in 1982, 1984, and 1987. From 1988 to 2001, he was a professor of Electrical and Computer Engineering at Purdue University, West Lafayette. He is currently the Department Head of Electrical and Computer Engineering at Colorado State University. He is a Fellow of the IEEE. A complete vita is available at: Venkataramanan Balakrishnan is Professor and Associate Head of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. He received the B.Tech degree in electronics and communication and the President of India Gold Medal from the Indian Institute of Technology, Madras, in 1985. He then attended Stanford University, where he received the M.S. degree in statistics and the Ph.D. degree in electrical engineering in 1992. He joined Purdue University in 1994 after post-doctoral research at Stanford, CalTech and the University of Maryland. His primary research interests are in convex optimization and large-scale numerical algebra, applied to engineering problems. Rodney G. Roberts received B.S. degrees in Electrical Engineering and Mathematics from Rose-Hulman Institute of Technology in 1987 and an MSEE and Ph.D. in Electrical Engineering from Purdue University in 1988 and 1992, respectively. From 1992 until 1994, he was a National Research Council Fellow at Wright Patterson Air Force Base in Dayton, Ohio. Since 1994 he has been at the Florida A&M University---Florida State University College of Engineering where he is currently a Professor of Electrical and Computer Engineering. His research interests are in the areas of robotics and image processing. Kishor Saitwal received the Bachelor of Engineering (B.E.) degree in Instrumentation and Controls from Vishwakarma Institute of Technology, Pune, India, in 1998. He was ranked Third in the Pune University and was recipient of National Talent Search scholarship. He received the M.S. and Ph.D. degrees from the Electrical and Computer Engineering department, Colorado State University, Fort Collins, in 2001 and 2006, respectively. He is currently with Behavioral Recognition Systems, Inc. performing research in computer aided video surveillance systems. His research interests include image/video processing, computer vision, and robotics. 相似文献
Anthony A. MaciejewskiEmail: |
Chu-Yin Chang received the B.S. degree in mechanical engineering from National Central University, Chung-Li, Taiwan, ROC, in 1988, the M.S. degree in electrical engineering from the University of California, Davis, in 1993, and the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, in 1999. From 1999--2002, he was a Machine Vision Systems Engineer with Semiconductor Technologies and Instruments, Inc., Plano, TX. He is currently the Vice President of Energid Technologies, Cambridge, MA, USA. His research interests include computer vision, computer graphics, and robotics. Anthony A. Maciejewski received the BSEE, M.S., and Ph.D. degrees from Ohio State University in 1982, 1984, and 1987. From 1988 to 2001, he was a professor of Electrical and Computer Engineering at Purdue University, West Lafayette. He is currently the Department Head of Electrical and Computer Engineering at Colorado State University. He is a Fellow of the IEEE. A complete vita is available at: Venkataramanan Balakrishnan is Professor and Associate Head of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. He received the B.Tech degree in electronics and communication and the President of India Gold Medal from the Indian Institute of Technology, Madras, in 1985. He then attended Stanford University, where he received the M.S. degree in statistics and the Ph.D. degree in electrical engineering in 1992. He joined Purdue University in 1994 after post-doctoral research at Stanford, CalTech and the University of Maryland. His primary research interests are in convex optimization and large-scale numerical algebra, applied to engineering problems. Rodney G. Roberts received B.S. degrees in Electrical Engineering and Mathematics from Rose-Hulman Institute of Technology in 1987 and an MSEE and Ph.D. in Electrical Engineering from Purdue University in 1988 and 1992, respectively. From 1992 until 1994, he was a National Research Council Fellow at Wright Patterson Air Force Base in Dayton, Ohio. Since 1994 he has been at the Florida A&M University---Florida State University College of Engineering where he is currently a Professor of Electrical and Computer Engineering. His research interests are in the areas of robotics and image processing. Kishor Saitwal received the Bachelor of Engineering (B.E.) degree in Instrumentation and Controls from Vishwakarma Institute of Technology, Pune, India, in 1998. He was ranked Third in the Pune University and was recipient of National Talent Search scholarship. He received the M.S. and Ph.D. degrees from the Electrical and Computer Engineering department, Colorado State University, Fort Collins, in 2001 and 2006, respectively. He is currently with Behavioral Recognition Systems, Inc. performing research in computer aided video surveillance systems. His research interests include image/video processing, computer vision, and robotics. 相似文献
3.
In this paper a novel scheme for color video compression using color transfer technique is proposed. Towards this, a new color
transfer mechanism for video using motion estimation is presented. Encoder and decoder architectures for the proposed compression
scheme are also presented. In this scheme, compression is achieved by firstly discarding chrominance information for all but
selected reference frames and then using motion prediction and discrete cosine transform (DCT) based quantization. At decompression
stage, the luminance-only frames are colored using chrominance information from the reference frames applying the proposed
color transfer technique. To integrate color transfer mechanism with hybrid compression scheme a new color transfer protocol
is defined. Both compression scheme and color transfer work in YCbCr color space.
Ritwik Kumar received his B.Tech. degree in Information and Communication Technology from Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India in 2005. Since 2005 he has been a Ph.D. student at the Center for Vision, Graphics and Medical Imaging at the Department of Computer and Information Science and Engineering at the University of Florida, Gainesville, FL, USA. His research interests include machine learning, color video processing and face recognition Suman K. Mitra is an Assistant Professor at the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. Dr. Mitra obtained his Ph.D. from the Indian Statistical Institute. Earlier, Dr. Mitra was with the Institute of Neural Computation at the University of California, San Diego, USA as a post-graduate researcher and with the Department of Mathematics at the Indian Institute of Technology, Bombay as an assistant professor. Dr. Mitra’s research interest includes image processing, pattern recognition, Bayesian networks and digital watermarking. Currently, Dr. Mitra is serving International Journal of Image and Graphics (IJIG) as an Associate Editor. Dr. Mitra is a life member of ISCA and a member of IEEE, and IUPRAI 相似文献
Ritwik KumarEmail: |
Ritwik Kumar received his B.Tech. degree in Information and Communication Technology from Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India in 2005. Since 2005 he has been a Ph.D. student at the Center for Vision, Graphics and Medical Imaging at the Department of Computer and Information Science and Engineering at the University of Florida, Gainesville, FL, USA. His research interests include machine learning, color video processing and face recognition Suman K. Mitra is an Assistant Professor at the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. Dr. Mitra obtained his Ph.D. from the Indian Statistical Institute. Earlier, Dr. Mitra was with the Institute of Neural Computation at the University of California, San Diego, USA as a post-graduate researcher and with the Department of Mathematics at the Indian Institute of Technology, Bombay as an assistant professor. Dr. Mitra’s research interest includes image processing, pattern recognition, Bayesian networks and digital watermarking. Currently, Dr. Mitra is serving International Journal of Image and Graphics (IJIG) as an Associate Editor. Dr. Mitra is a life member of ISCA and a member of IEEE, and IUPRAI 相似文献
4.
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
5.
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
6.
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
7.
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
8.
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
9.
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
10.
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
11.
We show how to create a music video automatically, using computable characteristics of the video and music to promote coherent
matching. We analyze the flow of both music and video, and then segment them into sequences of near-uniform flow. We extract
features from the both video and music segments, and then find matching pairs. The granularity of the matching process can
be adapted by extending the segmentation process to several levels. Our approach drastically reduces the skill required to
make simple music videos.
Jong-Chul Yoon received his B.S. and M.S. degree in Media from Ajou University in 2003 and 2005, respectively. He is currently a Ph.D. candidate in the Computer Science from Yonsei University. His research interests include computer animation, multi-media control, and geometric modeling. In-Kwon Lee received his B.S. degree in Computer Science from Yonsei University in 1989 and earned his M.S. and Ph.D. in Computer Science from POSTECH in 1992 and 1997, respectively. Currently, he is teaching and researching in the area of computer animation, geometric modeling, and computational music in Yonsei University. Siwoo Byun received his B.S. degree in Computer Science from Yonsei University in 1989 and earned his M.S. and Ph.D. in Computer Science from Korea Advanced Institute of Science and Technology (KAIST) in 1991 and 1999, respectively. Currently, he is teaching and researching in the area of distributed database systems, mobile computing, and fault-tolerant systems in Anyang University. 相似文献
Siwoo ByunEmail: |
Jong-Chul Yoon received his B.S. and M.S. degree in Media from Ajou University in 2003 and 2005, respectively. He is currently a Ph.D. candidate in the Computer Science from Yonsei University. His research interests include computer animation, multi-media control, and geometric modeling. In-Kwon Lee received his B.S. degree in Computer Science from Yonsei University in 1989 and earned his M.S. and Ph.D. in Computer Science from POSTECH in 1992 and 1997, respectively. Currently, he is teaching and researching in the area of computer animation, geometric modeling, and computational music in Yonsei University. Siwoo Byun received his B.S. degree in Computer Science from Yonsei University in 1989 and earned his M.S. and Ph.D. in Computer Science from Korea Advanced Institute of Science and Technology (KAIST) in 1991 and 1999, respectively. Currently, he is teaching and researching in the area of distributed database systems, mobile computing, and fault-tolerant systems in Anyang University. 相似文献
12.
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
13.
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
14.
With a plethora of models, systems and standards to choose for a basis of software process improvement, decisions on which
to adopt may depend on a number of factors. This paper presents an evolutionary and extremely cost effective approach to implementing
a software quality system that requires minimum resource and little disruption to programme delivery. The method presented,
achieved a 40% improvement in the level of implementation of the AWE plc software quality management system over a 5-year
period. A critical success factor is the treatment of the users’ of the defined software quality system as customers, understanding
their concerns and problems, and being responsive to them. The importance of a well designed system is highlighted together
with the essential and extensive consultation process required to gain buy-in and lay the foundation for cultural change.
This was supported with a helpful programme of facilitated self-assessment and sustained by a closely aligned training scheme.
As a consequence some of the cultural elements were changed from one of thoughtless “tick-in-the-box” compliance to one of
true understanding of the system requirements, true quality implementation, and subsequent added value.
Michael Elliott is a Chartered Engineer and member of the British Computer Society. He is the Sodftware Quality Manager at the Atomic Weapons Establishment (AWE) in the UK and is accountable to ensure the certification to ISO 9001:2000 for all software related activities. Mike’s particular interest is the intricacies of dealing with different people in a culturally diverse establishment, such as AWE. He has recently completed post graduate research at Loughborough University with Ray Dawson and Janet Edwards. His thesis was entitled “Achieving business excellence in software quality management”. The research investigated the practical nuances of the internal auditing, the adoption of self-assessment as a methodology, the cost effectiveness of training and the cost-benefits associated with implementing best practice in software quality management. Ray Dawson obtained a Bachelor’s degree in mathematics with engineering and a masters degree in engineering from Nottingham University before entering industry with Plessey Telecommunications in 1977. While working at the company he developed an interest in the working methods for software development as practiced in industry. This became a research interest when he joined Loughborough University as a lecturer in 1987. Other research interests are information systems and knowledge management which he now combines with his interest in industrial working practices to work with companies to improve their information and knowledge management systems. Ray Dawson is now a Senior Lecturer in the Department of Computer Science and leader of the Knowledge Management Research Group at Loughborough University in the UK, and is a Chartered Engineer and fellow of the British Computer Society. Janet Edwards has recently retired as a lecturer in Computer Science at Loughborough University. She has a Btech (Hons) degree in Metallurgical Engineering and Management and an MSc degree by research in Robotic Control from Loughborough University. She spent a number of years working as a software engineer in various organisations before returning to Loughborough. Her current research interests include Electronic Communication and E-commerce. 相似文献
Janet EdwardsEmail: |
Michael Elliott is a Chartered Engineer and member of the British Computer Society. He is the Sodftware Quality Manager at the Atomic Weapons Establishment (AWE) in the UK and is accountable to ensure the certification to ISO 9001:2000 for all software related activities. Mike’s particular interest is the intricacies of dealing with different people in a culturally diverse establishment, such as AWE. He has recently completed post graduate research at Loughborough University with Ray Dawson and Janet Edwards. His thesis was entitled “Achieving business excellence in software quality management”. The research investigated the practical nuances of the internal auditing, the adoption of self-assessment as a methodology, the cost effectiveness of training and the cost-benefits associated with implementing best practice in software quality management. Ray Dawson obtained a Bachelor’s degree in mathematics with engineering and a masters degree in engineering from Nottingham University before entering industry with Plessey Telecommunications in 1977. While working at the company he developed an interest in the working methods for software development as practiced in industry. This became a research interest when he joined Loughborough University as a lecturer in 1987. Other research interests are information systems and knowledge management which he now combines with his interest in industrial working practices to work with companies to improve their information and knowledge management systems. Ray Dawson is now a Senior Lecturer in the Department of Computer Science and leader of the Knowledge Management Research Group at Loughborough University in the UK, and is a Chartered Engineer and fellow of the British Computer Society. Janet Edwards has recently retired as a lecturer in Computer Science at Loughborough University. She has a Btech (Hons) degree in Metallurgical Engineering and Management and an MSc degree by research in Robotic Control from Loughborough University. She spent a number of years working as a software engineer in various organisations before returning to Loughborough. Her current research interests include Electronic Communication and E-commerce. 相似文献
15.
Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems,
where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems
requires significant effort due to the large number of possible causes. This study presents an approach to detect problems
and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures
of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can
be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate
the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices
can be monitored simultaneously to facilitate the causal analysis of a software process.
Ching-Pao Chang is a PhD candidate in Computer Science & Information Engineering at the National Cheng-Kung University, Taiwan. He received his MA from the University of Southern California in 1998 in Computer Science. His current work deals with the software process improvement and defect prevention using machine learning techniques. Chih-Ping Chu is Professor of Software Engineering in Department of Computer Science & Information Engineering at the National Cheng-Kung University (NCKU) in Taiwan. He received his MA in Computer Science from the University of California, Riverside in 1987, and his Doctorate in Computer Science from Louisiana State University in 1991. He is especially interested in parallel computing and software engineering. 相似文献
Chih-Ping ChuEmail: |
Ching-Pao Chang is a PhD candidate in Computer Science & Information Engineering at the National Cheng-Kung University, Taiwan. He received his MA from the University of Southern California in 1998 in Computer Science. His current work deals with the software process improvement and defect prevention using machine learning techniques. Chih-Ping Chu is Professor of Software Engineering in Department of Computer Science & Information Engineering at the National Cheng-Kung University (NCKU) in Taiwan. He received his MA in Computer Science from the University of California, Riverside in 1987, and his Doctorate in Computer Science from Louisiana State University in 1991. He is especially interested in parallel computing and software engineering. 相似文献
16.
An experimental investigation of personality types impact on pair effectiveness in pair programming 总被引:1,自引:1,他引:0
Panagiotis Sfetsos Ioannis Stamelos Lefteris Angelis Ignatios Deligiannis 《Empirical Software Engineering》2009,14(2):187-226
In this paper, pair programming is empirically investigated from the perspective of developer personalities and temperaments
and how they affect pair effectiveness. A controlled experiment was conducted to investigate the impact of developer personalities
and temperaments on communication, pair performance and pair viability-collaboration. The experiment involved 70 undergraduate
students and the objective was to compare pairs of heterogeneous developer personalities and temperaments with pairs of homogeneous
personalities and temperaments, in terms of pair effectiveness. Pair effectiveness is expressed in terms of pair performance, measured by communication, velocity, design correctness and passed acceptance tests, and pair collaboration-viability measured by developers’ satisfaction, knowledge acquisition and participation. The results have shown that there is important
difference between the two groups, indicating better communication, pair performance and pair collaboration-viability for
the pairs with heterogeneous personalities and temperaments. In order to provide an objective assessment of the differences
between the two groups of pairs, a number of statistical tests and stepwise Discriminant Analysis were used.
Panagiotis Sfetsos is an Assistant Professor at the Department of Informatics at the Alexander Technological Educational Institute of Thessaloniki, Greece. He received his B.Sc. in Computer Science and Statistics from the University of Uppsala, Sweden (1981), and the Ph.D. degree in Computer Science from the Aristotle University of Thessaloniki (2007). His Ph.D. Thesis was on “Experimentation in Object Oriented Technology and Agile Methods”. His research interests include empirical software evaluation, measurement, testing, quality, agile methods and especially extreme programming. Ioannis G. Stamelos is an Associate Professor of Computer Science at the Aristotle University of Thessaloniki, Dept. of Informatics. He received a degree in Electrical Engineering from the Polytechnic School of Thessaloniki (1983) and the Ph. D. degree in Computer Science from the Aristotle University of Thessaloniki (1988). He teaches object-oriented programming, software engineering, software project management and enterprise information systems at the graduate and postgraduate level. His research interests include empirical software evaluation and management, software education and open source software engineering. He is author of 90 scientific papers and member of the IEEE Computer Society. Lefteris Angelis received his B.Sc. and Ph.D. degree in Mathematics from Aristotle University of Thessaloniki (A.U.Th.). He works currently as an Assistant Professor at the Department of Informatics of A.U.Th. His research interests involve statistical methods with applications in software engineering and information systems, computational methods in mathematics and statistics, planning of experiments and simulation techniques. Ignatios Deligiannis is an Associate Professor at Alexander Technological Education Institute of Thessaloniki, Greece. His main interests are Object-Oriented software methods, and in particular design assessment and measurement. He received his B.Sc. in Computer Science from Lund University, Sweden, in 1979, and then worked for several years in software development at Siemens Telecommunications industry. He was member of ESERG (Empirical Software Engineering Research Group at Bournemouth University, UK). Currently, he is a research partner of Software Engineering Group::Plase laboratory, Aristotle University of Thessaloniki, Greece. 相似文献
Ignatios DeligiannisEmail: |
Panagiotis Sfetsos is an Assistant Professor at the Department of Informatics at the Alexander Technological Educational Institute of Thessaloniki, Greece. He received his B.Sc. in Computer Science and Statistics from the University of Uppsala, Sweden (1981), and the Ph.D. degree in Computer Science from the Aristotle University of Thessaloniki (2007). His Ph.D. Thesis was on “Experimentation in Object Oriented Technology and Agile Methods”. His research interests include empirical software evaluation, measurement, testing, quality, agile methods and especially extreme programming. Ioannis G. Stamelos is an Associate Professor of Computer Science at the Aristotle University of Thessaloniki, Dept. of Informatics. He received a degree in Electrical Engineering from the Polytechnic School of Thessaloniki (1983) and the Ph. D. degree in Computer Science from the Aristotle University of Thessaloniki (1988). He teaches object-oriented programming, software engineering, software project management and enterprise information systems at the graduate and postgraduate level. His research interests include empirical software evaluation and management, software education and open source software engineering. He is author of 90 scientific papers and member of the IEEE Computer Society. Lefteris Angelis received his B.Sc. and Ph.D. degree in Mathematics from Aristotle University of Thessaloniki (A.U.Th.). He works currently as an Assistant Professor at the Department of Informatics of A.U.Th. His research interests involve statistical methods with applications in software engineering and information systems, computational methods in mathematics and statistics, planning of experiments and simulation techniques. Ignatios Deligiannis is an Associate Professor at Alexander Technological Education Institute of Thessaloniki, Greece. His main interests are Object-Oriented software methods, and in particular design assessment and measurement. He received his B.Sc. in Computer Science from Lund University, Sweden, in 1979, and then worked for several years in software development at Siemens Telecommunications industry. He was member of ESERG (Empirical Software Engineering Research Group at Bournemouth University, UK). Currently, he is a research partner of Software Engineering Group::Plase laboratory, Aristotle University of Thessaloniki, Greece. 相似文献
17.
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
18.
19.
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
20.
Denys Poshyvanyk Andrian Marcus Rudolf Ferenc Tibor Gyimóthy 《Empirical Software Engineering》2009,14(1):5-32
Coupling is an important property of software systems, which directly impacts program comprehension. In addition, the strength
of coupling measured between modules in software is often used as a predictor of external software quality attributes such
as changeability, ripple effects of changes and fault-proneness. This paper presents a new set of coupling measures for Object-Oriented
(OO) software systems measuring conceptual coupling of classes. Conceptual coupling is based on measuring the degree to which
the identifiers and comments from different classes relate to each other. This type of relationship, called conceptual coupling,
is measured through the use of Information Retrieval (IR) techniques. The proposed measures are different from existing coupling
measures and they capture new dimensions of coupling, which are not captured by the existing coupling measures. The paper
investigates the use of the conceptual coupling measures during change impact analysis. The paper reports the findings of
a case study in the source code of the Mozilla web browser, where the conceptual coupling metrics were compared to nine existing
structural coupling metrics and proved to be better predictors for classes impacted by changes.
Denys Poshyvanyk is an Assistant Professor at the College of William and Mary in Virginia. He received his Ph.D. degree in Computer Science from Wayne State University in 2008. He also obtained his MS and MA degrees in Computer Science from the National University of Kyiv-Mohyla Academy, Ukraine and Wayne State University in 2003 and 2006, respectively. His research interests are in software engineering, software maintenance and evolution, program comprehension, reverse engineering, software repository mining, source code analysis and metrics. He is member of the IEEE and ACM. Andrian Marcus is currently an Assistant Professor at the Department of Computer Science at Wayne State University, Detroit. His research interests include software evolution, program understanding, and software visualization, in particular using information retrieval techniques to support software engineering tasks. Since 2005, he has been serving on the steering committee of the IEEE International Conference on Software Maintenance (ICSM) and he will be Program Co-Chair for the 17th IEEE International Conference on Program Comprehension (ICPC 2009) and the 26th IEEE International Conference on Software Maintenance (ICSM 2010). He is the recipient of a Fulbright Junior Research Fellowship in 1997. Rudolf Ferenc is an Assistant Professor at the University of Szeged in Hungary. His research interests include source code analysis, modeling, measurement and design pattern recognition. He is also interested in software quality assurance and open source software development. He is Program Co-Chair of the 13th European Conference on Software Maintenance and Reengineering (CSMR 2009). Tibor Gyimóthy is the head of the Software Engineering Department at the University of Szeged in Hungary. His research interests include program comprehension, slicing, reverse engineering and compiler optimization. He has published over 70 papers in these areas and was the leader of several software engineering R&D projects. He was the Program Co-Chair of the 21th International Conference on Software Maintenance (ICSM 2005). 相似文献
Tibor GyimóthyEmail: |
Denys Poshyvanyk is an Assistant Professor at the College of William and Mary in Virginia. He received his Ph.D. degree in Computer Science from Wayne State University in 2008. He also obtained his MS and MA degrees in Computer Science from the National University of Kyiv-Mohyla Academy, Ukraine and Wayne State University in 2003 and 2006, respectively. His research interests are in software engineering, software maintenance and evolution, program comprehension, reverse engineering, software repository mining, source code analysis and metrics. He is member of the IEEE and ACM. Andrian Marcus is currently an Assistant Professor at the Department of Computer Science at Wayne State University, Detroit. His research interests include software evolution, program understanding, and software visualization, in particular using information retrieval techniques to support software engineering tasks. Since 2005, he has been serving on the steering committee of the IEEE International Conference on Software Maintenance (ICSM) and he will be Program Co-Chair for the 17th IEEE International Conference on Program Comprehension (ICPC 2009) and the 26th IEEE International Conference on Software Maintenance (ICSM 2010). He is the recipient of a Fulbright Junior Research Fellowship in 1997. Rudolf Ferenc is an Assistant Professor at the University of Szeged in Hungary. His research interests include source code analysis, modeling, measurement and design pattern recognition. He is also interested in software quality assurance and open source software development. He is Program Co-Chair of the 13th European Conference on Software Maintenance and Reengineering (CSMR 2009). Tibor Gyimóthy is the head of the Software Engineering Department at the University of Szeged in Hungary. His research interests include program comprehension, slicing, reverse engineering and compiler optimization. He has published over 70 papers in these areas and was the leader of several software engineering R&D projects. He was the Program Co-Chair of the 21th International Conference on Software Maintenance (ICSM 2005). 相似文献