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Keynote Lectures

To be announced soon.
Rita Cucchiara, University of Modena and Reggio Emilia, Italy

Time Evolving Networks in Finance and Medicine
Edwin Hancock, York University, United Kingdom

Biomedical Imaging - Challenges and Potentials
Xiaoyi Jiang, University of Münster, Germany

On Location and Registration Fiducials: Their Analysis and Design
Alfred Bruckstein, Technion, Israel

 

To be announced soon.

Rita Cucchiara
University of Modena and Reggio Emilia
Italy
 

Brief Bio
Available soon.


Abstract
Available soon.



 

 

Time Evolving Networks in Finance and Medicine

Edwin Hancock
York University
United Kingdom
 

Brief Bio
Edwin R. Hancock holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015. From 1981-1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period,  he worked on high energy physics experiments at the Stanford Linear Accelarator Center (SLAC) providing the first measurements of charmed particle lifetimes. He also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical and pattern recognition. He is currently working on graph matching, shape-from-X, image databases, and statistical learning theory. His work has found applications in areas such as radar terrain analysis, seismic section analysis, remote sensing, and medical imaging. He has published about 170 journal papers and 610 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006,  BMVC 2007 and ICIAP in 2009 and 2015. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. In 2016 he became a fellow of the IEEE and was named  Distinguished Fellow by the British Machine Vision Association.  He is currently Editor-in-Chief of the journal Pattern Recognition, and was founding Editor-in-Chief of IET Computer Vision from 2006 until 2012.  He has also been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, Image and Vision Computing, and the International Journal of Complex Networks. He has been Conference Chair for BMVC in 1994 and  Progrmme Chair in 2016, Track Chair for ICPR in  2004 and 2016 and Area Chair at ECCV 2006 and CVPR in  2008 and 2014, and in 1997 established the EMMCVPR workshop series. He has been a Governing Board Member of the IAPR since 2006, and is currently Vice President of the Association.


Abstract
This talk focusses on how to use network entropy as a means of characterising network structure and investigating the relationship between changes in network structure and function with time. Examples are presented on network data extracted from the data for the New York Stock Exchange. We show how the entropic characterisation can be extended to develop Euler- Lagrange equations which describe the evolution of the node degree distribution, and can be used to predict the evolution of network structure with time. If time permits, we will also describe how to  extend our model to include quantum spin statistics, and explore how Bose-Einstein and Fermi-Dirac statistics modify the evolution of network structure. We demonstrate some of the utility of the proposed methods on fMRI images of Alzheimer brains.



 

 

Biomedical Imaging - Challenges and Potentials

Xiaoyi Jiang
University of Münster
Germany
 

Brief Bio
Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree from University of Bern, Switzerland. He was associate professor at Technical University of Berlin and since 2002 full Professor at University of Münster, Germany. Currently, he is the dean of Faculty of Mathematics and Computer Science at University of Münster. He is a PI and research area leader of the Cluster of Excellence “Cells in Motion – Imaging to understand cellular behaviour in organisms” established by the German Research Foundation DFG in 2013. He is Editor-in-Chief of International Journal of Pattern Recognition and Artificial Intelligence. In addition, he also serves on the Advisory Board and Editorial Board of several journals, including IEEE Transactions on Medical Imaging, International Journal of Neural Systems, Pattern Analysis and Applications, and Pattern Recognition. His research interests include biomedical imaging, 3D image analysis, and structural pattern recognition. He is a Senior Member of IEEE and Fellow of IAPR.


Abstract
Imaging has become an indispensable tool in biology and medicine for both basic research and clinical practice. The specific image characteristics and problems in these fields have motivated researchers to develop novel concepts and algorithms. This talk emphasizes the fundamental research view of biomedical imaging and discusses a number of related challenges, concepts, and algorithms. In addition to the traditional computer vision approaches, another focus will be given to machine learning based approaches. In particular, Barista (an open-source graphical high-level interface for the Caffe deep learning framework) and its application to biomedical imaging will be presented.
Besides the information processing view the imposing development in biomedical imaging also provides a driving force for life sciences from a Galisonian perspective.



 

 

On Location and Registration Fiducials: Their Analysis and Design

Alfred Bruckstein
Technion
Israel
 

Brief Bio
Alfred M. Bruckstein, born in Transylvania, Romania, in 1954,  received his BSc and MSc degrees at the Technion, Haifa, in 1976 and 1980, respectively and then earned a Ph.D. degree in Electrical Engineering in Stanford University, California in 1984, his advisor being Professor Thomas Kailath.  
From October 1984 he has been with the Technion, where he presently holds of the Ollendorff Chair in Science, in the Computer Science Department.  His research interests are in Ants and Swarm Robotics, Signal and Image Processing, Image Analysis and Synthesis, Pattern Recognition, and various aspects of Applied Geometry. Professor Bruckstein authored and co-authored over one hundred and fifty journal papers in the fields of interest mentioned. From 2002 till 2005 he served as the Dean of  The Technion Graduate School, and from 2006-2011 as the Head of Technion’s Excellence Program for Undergraduate Studies.  Since 2009, he is also affiliated with the Nanyang Technological University in Singapore, as a Visiting Professor in the Department of Mathematics. 
Professor Bruckstein is a member of the AMS, and MAA, and a SIAM Fellow for contributions to Signal Processing, Image Analysis, and Ant Robotics, and received SIAM’s 2014 SIAG-Imaging Science Prize (with David Donoho and Michael Elad, for the paper “From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images”).


Abstract
A location/registration fiducial is a shape or pattern designed to provide, via a sensing or imaging device (usually a camera), information about the absolute or relative location of objects in space. This talk will discuss methods for the design of such fiducials, and ways to mathematically analyze and predict the  localization performance obtained when sensing them.



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