skip to primary navigationskip to content

FUSION 2018

21st International Conference on Information Fusion - 10 - 13 July 2018

Studying at Cambridge

 

Keynote speakers

Keynote presentations will include:

Wednesday 11 July

Variational Inference and Gaussian Processes

Professor Carl Edward Rasmussen, Department of Engineering, University of Cambridge

Presentation available here

Thursday 12 July

Fusion of Multi-band Images Using Bayesian Approaches: Beyond Pansharpening

Professor Jean-Yves Tourneret, University of Toulouse

Presentation available here

Friday 13 July

25 years of particles and other random points

Dr Neil Gordon, The Defence Science and Technology Group, Department of Defence, David J Salmond and Professor Sir Adrian Smith, University of London

Presentation available here


 

Fusion of Multi-band Images Using Bayesian Approaches: Beyond Pansharpening

Professor Jean-Yves Tourneret, University of Toulouse

This talk will discuss several methods for fusing high spectral resolution images (such as hyperspectral images) and high spatial resolution images (such as panchromatic images) in order to provide images with improved spectral and spatial resolutions. The first part will be devoted to summarizing the main image fusion methods based on component substitution, multiresolution analysis, Bayesian inference and matrix factorization. The second part will present recent Bayesian fusion strategies exploiting prior information about the target image to be recovered, constructed by interpolation or by using dictionary learning techniques. The resulting Bayesian estimators can be computed by using samples generated by Markov chain Monte Carlo algorithms, by exploiting the efficiency of alternating optimization methods or by solving Sylvester matrix equations.  

Professor Jean-Yves Tourneret 

Jean-Yves TOURNERET (SM’08) received the ingénieur degree in electrical engineering from the Ecole Nationale Supérieure d’Electronique, d’Electrotechnique, d’Informatique, d’Hydraulique et des Télécommunications (ENSEEIHT) de Toulouse in 1989 and the PhD degree from the National Polytechnic Institute from Toulouse in 1992. He is currently a professor in the university of Toulouse (ENSEEIHT) and a member of the IRIT laboratory (UMR 5505 of the CNRS). His research activities are centered around statistical signal and image processing with a particular interest to Bayesian and Markov chain Monte Carlo (MCMC) methods. He has been involved in the organization of several conferences including the European conference on signal processing EUSIPCO'02 (program chair), the international conference ICASSP’06 (plenaries), the statistical signal processing workshop SSP’12 (international liaisons), the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing CAMSAP 2013 (local arrangements), the statistical signal processing workshop SSP'2014 (special sessions), the workshop on machine learning for signal processing MLSP’2014 (special sessions). He has been the general chair of the CIMI workshop on optimization and statistics in image processing hold in Toulouse in 2013 (with F. Malgouyres and D. Kouamé), of the International Workshop on Computational Advances in Multi-Sensor Adaptive Processing in 2015 (with P. Djuric) and 2019 (with D. Brie). He has been a member of different technical committees including the Signal Processing Theory and Methods (SPTM) committee of the IEEE Signal Processing Society (2001-2007, 2010-2015) and the EURASIP SAT committee on Theoretical and Methodological Trends in Signal Processing (TMTSP). He has been serving as an associate editor for the IEEE Transactions on Signal Processing (2008-2011, 2015-present) and for the EURASIP journal on Signal Processing (2013-present).

 

Variational Inference and Gaussian Processes

Professor Carl Edward Rasmussen, Department of Engineering, University of Cambridge

Gaussian Processes are a principled, practical, probabilistic approach to learning in flexible non-parametric models and have found numerous applications in regression, classification, unsupervised learning and reinforcement learning. Inference, learning and prediction can be done exactly on small data sets with Gaussian likelihood. In more realistic application with large scale data and more complicated likelihoods approximations are necessary. The variational framework for approximate inference in Gaussian processes has emerged recently as a highly effective and practical tool. I will review and demonstrate the capabilities of this framework.

Professor Carl Edward Rasmussen

Department of Engineering, University of Cambridge, UK

Carl Edward Rasmussen is professor of machine learning in the Department of Engineering at the University of Cambridge. He received an MSc in Electrical Engineering from the Technical University of Denmark in 1993 and did his PhD with Geoff Hinton Computer Science at the University of Toronto in 1996. Since then he has been a post doc at the Technical University of Denmark, a Senior Research Fellow at the Gatsby Computational Neuroscience Unit at University College London, and a research group leader at the Max Planck Institute of Biological Cybernetics in Tübingen, Germany. In 2007 he moved to Cambridge, where he is now professor of Machine Learning and head of the Computational and Biological Learning Lab. He is chairman of Cambridge AI company PROWLER.io.

25 years of particles and other random points

Dr Neil Gordon, The Defence Science and Technology Group, Department of Defence

David J Salmond

Professor Sir Adrian Smith, University of London

A basic form of a Monte Carlo Bayesian recursive filter, which came to be known as a bootstrap filter or a particle filter, was presented 25 years ago.

The key advantage of this filter is that it does not rely on the highly restrictive linear-Gaussian assumptions that underlie the Kalman filter and its variants.

Since then, the particle scheme has been developed, enhanced and applied by researchers in many different fields ranging from the original motivation of target tracking to navigation, robotics, econometrics and weather forecasting.

In this presentation, we shall describe the state of the art in Monte Carlo methods for Bayesian estimation problems in the early 1990’s and indicate how this was extended to dynamic estimation with an evolving state vector.

The circumstances of the development of the filter and some initial test examples will be reviewed with some discussion of the strengths and weaknesses of the approach.
Finally, we shall discuss recent developments, applications and possible future directions.

Reference:
N.J. Gordon, D.J. Salmond and A.F.M. Smith, “Novel approach to nonlinear / non-Gaussian Bayesian state estimation”, IEE Proceedings-F on Radar, Sonar and Navigation, Vol 140, No 2, April 1993, pp 107-113.

Dr Neil Gordon

The Defence Science and Technology Group, Department of Defence, Australia

Neil Gordon received a PhD in Statistics from Imperial College London in 1993. He was with the Defence Evaluation and Research Agency in the UK from 1988-2002 working on missile guidance and statistical data processing. In 2002 he moved to the Defence Science and Technology Organisation in  Australia where he currently leads the Data and Information Fusion research group. In 2014 he became an honorary Professor with the School of Information Technology and Electrical Engineering at the University of Queensland. He is the co-author/co-editor of two books on particle filtering and one on search zone calculations for missing Malaysian airlines flight MH370.

DavidS.jpg

David J Salmond, BSc, DPhil, CEng, FIEE

David Salmond joined the Royal Aircraft Establishment in 1977 as a Scientific Officer. His initial research was on the application of modern control theory to weapon guidance. From this work, together with the missile guidance group, he developed general techniques for tracking and guidance, especially for uncertain systems. In particular, with co-workers, he developed a general Bayesian acquisition / selection scheme for dense and cluttered scenarios. He was also a co-developer of the particle filter method for nonlinear dynamic estimation. The basic scheme has been widely taken up and developed by both the academic community and applied engineers.

He has worked closely with the UK tracking community for many years. He has organised and chaired conferences, served on conference committees and reviews papers for the IEEE, AIAA, IET and other control and data fusion journals. He has published over 150 company reports and open papers.

David retired from QinetiQ as a Senior Fellow in May 2016. He is now a consultant for QinetiQ under the “Friend of QinetiQ” scheme. He has worked for QinetiQ since its foundation in 2001, apart from four years with DSTL (Defence Science and Technology Laboratory) from 2006 to 2010. Prior to 2001, he worked (as a Civil Servant) for QinetiQ’s predecessor institutions.

asmith.jpg

Professor Sir Adrian Smith

From 1 September 2012 Professor Smith was appointed Vice-Chancellor of the University of London. He was previously Director General, Knowledge and Innovation in BIS, having, from 2008, been Director General, Science and Research originally in DIUS and subsequently in BIS.

Professor Smith has also worked with the UK Higher Education Funding and Research Councils and was appointed Deputy Chair of the UK Statistics Authority from 1 September 2012. From 1 August 2014, he was appointed Chair of the Board of the Diamond Synchrotron at Harwell. He is also the Chair of the Council for Mathematical Sciences.

Professor Smith is a past President of the Royal Statistical Society and was elected a Fellow of the Royal Society in 2001 in recognition of his contribution to statistics. In 2003-04 he undertook an Inquiry into Post-14 Mathematics Education for the UK Secretary of State for Education and Skills and has recently undertaken, on behalf of HMT and the DfE, a 16-18 Maths Review. In 2006 he completed a report for the UK Home Secretary on the issue of public trust in Crime Statistics. He received a knighthood in the 2011 New Year Honours list.