1. Call for papers
  2. Important dates
  3. Keynote speakers
  4. Paper submission
  5. Technical program
  6. Registration
  7. Presentation guidelines
  8. Organising committee
  9. Venue and travel

Keynote speakers

Intelligent Optimisation and the Bees Algorithm, Professor Duc Trong Pham

Many real-world engineering problems require the manipulation of a number of system variables in order to optimise a given quality parameter such as the reliability or accuracy of a process, or the cost or performance of a product. Optimisation will become even more important as resources diminish. When the number of variables is large, it can be difficult to locate the optimal solution.
The Bees Algorithm models the foraging behaviour of a swarm of honeybees in order to solve complex optimisation problems. The algorithm performs a combination of exploitative neighbourhood search and random explorative search. In this keynote speech, we will review different formulations of the Bees Algorithm together with other swarm-based optimisation algorithms and compare the effectiveness of the Bees Algorithm to that of three other biologically inspired search methods. We will review applications of the Bees Algorithm in engineering and manufacture and demonstrate its effectiveness at finding solutions to multi-modal optimisation problems.

Speaker Biography:

Duc Truong Pham is the Head of the School of Mechanical Engineering at the University of Birmingham. He has made wide-ranging contributions to the theory and practice of mechanical, manufacturing and systems engineering.

His academic output includes more than 500 technical papers and 15 books. He has supervised over 100 PhD theses to completion. He has won in excess of £30M in external research grants and contracts.

In addition to pursuing and leading research, he has also been very active with knowledge transfer to industry, applying the results of his work to help multinational companies and SMEs to generate wealth and create and safeguard jobs.

He has lectured extensively abroad on his research and has acted as a consultant to several major companies. He was Professeur Invité at École Centrale de Paris, Consulting Professor at HUST (China), Erskine Visiting Fellow at the University of Canterbury (New Zealand), Visiting Professor at the Université Paul Verlaine (France) and Visiting Professor at King Saud University (Saudi Arabia). He is currently a Strategic Scientist at Wuhan University of Technology.

He is the recipient of several prizes including the Sir Joseph Whitworth prize awarded by the Institution of Mechanical Engineers in 1996 and 2000 and the Institution's Thomas Stephens Group Prize in 2001 and 2003 and Donald Julius Groen Prize in 2004, and the 5th ICMR Best Paper Prize in 2007.

He is a Fellow of the Royal Academy of Engineering, Learned Society of Wales, Society of Manufacturing Engineers, Institution of Engineering and Technology, and Institution of Mechanical Engineers.

He was made an OBE in the 2003 New Year's Honours List for his services to Engineering.  

Trends and Perspectives in Video Compression, Professor Jens-Rainer Ohm


The talk will firstly give a status report about recent developments in video compression standardization, particularly HEVC and its recent/ongoing extensions. In the second part, perspectives for achieving even higher compression will be discussed. This includes, e.g., improved motion compensation and intra picture compression models, as well as synthesis of content based on perceptual criteria. 

Speaker Biography: 

Jens-Rainer Ohm received the Dipl.-Ing. degree in 1985, the Dr.-Ing. degree in 1990, and the habil. degree in 1997, all from Technical University of Berlin (TUB), Germany. From 1985 to 1995, he was a research and teaching assistant with the Institute of Telecommunications at TUB. Between 1992 and 2000, he has also served as lecturer on topics of digital image processing, coding and transmission at TUB. From 1996 to 2000, he was project manager/coordinator at the Image Processing Department of Heinrich Hertz Institute (HHI) in Berlin.

In 2000, he was appointed full professor and since then holds the chair position of the Institute of Communication Engineering at RWTH Aachen University, Germany. His research and teaching activities cover the areas of motion-compensated, stereoscopic and 3-D image processing, multimedia signal coding and content description, transmission of video signals over mobile networks, as well as general topics of signal processing and digital communication systems. Since 1998, he participates in the work of the Moving Picture Experts Group (MPEG), where he has been contributing to the development of MPEG-4 (Video and AVC) and MPEG-7 standards. He is chair of the ISO/IEC WG 11 (MPEG) Video Subgroup since May 2002. From January 2005 until November 2009, he was also co-chairing the Joint Video Team (JVT) of MPEG and ITU-T SG 16 VCEG. Currently, he is co-chairing the Joint Collaborative Team on Video Coding (JCT-VC) of ISO and ITU-T, with intended mandate of developing the next generation of high-efficiency video coding technology. Prof. Ohm has authored textbooks on multimedia signal processing, analysis and coding, on communications engineering and signal transmission, as well as numerous papers in the various fields mentioned above.

He is member of various professional organizations including IEEE, VDE/ITG, EURASIP and AES.

Learning for Future Video, Professor Aggelos Katsaggelos


Learning has made it possible to unleash the power of data. We have moved away from the detailed modeling of a system or a phenomenon of interest thanks to the abundance of data as well as the huge improvements in processing power. With approaches like dictionary learning we can discover linear relationships between the input and output. On the other hand, recent advancements in deep learning have made it possible to discover non-linear relationships. As one of the examples in this talk we discuss the application of dictionary and deep learning to the video super-resolution problem. We describe a multiple-frame algorithm based on dictionary learning and motion estimation. We further describe the use of a convolutional neural network that is trained on both the spatial and temporal dimensions of videos to enhance their resolution. We demonstrate experimentally the effectiveness of these approaches. We finally discuss future research directions on the topic of learning.


Aggelos K. Katsaggelos received the Diploma degree in electrical and mechanical engineering from the Aristotelian University of Thessaloniki, Greece, in 1979, and the M.S. and Ph.D. degrees in Electrical Engineering from the Georgia Institute of Technology, in 1981 and 1985, respectively.

In 1985, he joined the Department of Electrical Engineering and Computer Science at Northwestern University, where he is currently a Professor holder of the AT&T chair. He was previously the holder of the Ameritech Chair of Information Technology (1997–2003). He is also the Director of the Motorola Center for Seamless Communications, a member of the Academic Staff, NorthShore University Health System, an affiliated faculty at the Department of Linguistics and he has an appointment with the Argonne National Laboratory.

He has published extensively in the areas of multimedia signal processing and communications (over 250 journal papers, 500 conference papers and 40 book chapters) and he is the holder of 25 international patents. He is the co-author of Rate-Distortion Based Video Compression (Kluwer, 1997), Super-Resolution for Images and Video (Claypool, 2007), Joint Source-Channel Video Transmission (Claypool, 2007), and Machine Learning, Optimization, and Sparsity (Cambridge University Press, forthcoming). He has supervised 50 Ph.D. theses so far.

Among his many professional activities Prof. Katsaggelos was Editor-in-Chief of the IEEE Signal Processing Magazine (1997–2002), a BOG Member of the IEEE Signal Processing Society (1999–2001), a member of the Publication Board of the IEEE Proceedings (2003-2007), and he is currently a Member of the Award Board of the IEEE Signal Processing Society. He is a Fellow of the IEEE (1998) and SPIE (2009) and the recipient of the IEEE Third Millennium Medal (2000), the IEEE Signal Processing Society Meritorious Service Award (2001), the IEEE Signal Processing Society Technical Achievement Award (2010), an IEEE Signal Processing Society Best Paper Award (2001), an IEEE ICME Paper Award (2006), an IEEE ICIP Paper Award (2007), an ISPA Paper Award (2009), and a EUSIPCO paper award (2013). He was a Distinguished Lecturer of the IEEE Signal Processing Society (2007–2008).

Vision for Robotic Surgery, Guang-Zhong Yang


Robotic surgery is a rapidly developing field in recent years with recognised commercial growth and an increasing range of operations. This keynote lecture outlines major technical challenges, as well as new research opportunities in robotic surgery and how IWSSIP community can contribute towards its future advances, particularly in imaging and vision for intra-operative guidance. It will cover the latest developments in surgical robots and new applications of surgical vision, as well as the use of new imaging techniques for integrating cellular level information into in vivo, in situ tissue characterisation, functional assessment, and intraoperative guidance.

Speaker Biography:

Professor Guang-Zhong Yang (FREng, FIEEE, FIET, FAIMBE, FIAMBE, FMICCAI, FCGI) is director and co-founder of the Hamlyn Centre for Robotic Surgery, Deputy Chairman of the Institute of Global Health Innovation, Imperial College London, UK. Professor Yang also holds a number of key academic positions at Imperial – he is Director and Founder of the Royal Society/Wolfson Medical Image Computing Laboratory, co-founder of the Wolfson Surgical Technology Laboratory, Chairman of the Centre for Pervasive Sensing. He is a Fellow of the Royal Academy of Engineering, fellow of IEEE, IET, AIMBE and a recipient of the Royal Society Research Merit Award and listed in The Times Eureka ‘Top 100’ in British Science.

Professor Yang’s main research interests are in medical imaging, sensing and robotics. In imaging, he is credited for a number of novel MR phase contrast velocity imaging and computational modelling techniques that have transformed in vivo blood flow quantification and visualization. These include the development of locally focused imaging combined with real-time navigator echoes for resolving respiratory motion for high-resolution coronary-angiography, as well as MR dynamic flow pressure mapping for which he received the ISMRM I. I Rabi Award. He pioneered the concept of perceptual docking for robotic control, which represents a paradigm shift of learning and knowledge acquisition of motor and perceptual/cognitive behaviour for robotics, as well as the field of Body Sensor Network (BSN) for providing personalized wireless monitoring platforms that are pervasive, intelligent, and context-aware.

Recent Developments in SHM based on Time Series Analysis


Many of the algorithms used for Structural Health Monitoring (SHM) are based on or motivated by time series analysis. Quite often, detection methods are variants of approaches developed within the Statistical Process Control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data is often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly-varying trends on the data or in abrupt switches between regimes. Recent work in time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this presentation. Another issue in time series analysis is indirectly related to the assumption of linear behaviour of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also.

Speaker Biography:

Professor Worden began academic life as a theoretical physicist, with a degree from York University and a PhD in Mechanical Engineering from Heriot-Watt University eventually followed. A period of research at Manchester University led to an appointment at the University of Sheffield in 1995, where he has happily remained since.

Keith's research is concerned with applications of advanced signal processing and machine learning methods to structural dynamics. The primary application is in the aerospace industry, although there has also been interaction with ground transport and offshore industries.

One of the research themes concerns non-linear systems. The research conducted here is concerned with assessing the importance of non-linear modelling within a given context and formulating appropriate methods of analysis. The analysis of non-linear systems can range from the fairly pragmatic to the extremes of mathematical complexity. The emphasis within the research group here is on the pragmatic and every attempt is made to maintain contact with engineering necessity.

Another major activity within the research group concerns structural health monitoring for aerospace systems and structures. The research is concerned with developing automated systems for inspection and diagnosis, with a view to reducing the cost-of-ownership of these high integrity structures. The methods used are largely adapted from pattern recognition and machine learning; often the algorithms make use of biological concepts e.g. neural networks, genetic algorithms and ant-colony metaphors. The experimental approaches developed range from global inspection using vibration analysis to local monitoring using ultrasound.

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City, University of London is an independent member institution of the University of London. Established by Royal Charter in 1836, the University of London consists of 18 independent member institutions with outstanding global reputations and several prestigious central academic bodies and activities.