Keynotes

Karen Egiazarian (Eguiazarian) 

Professor, Computational Imaging Group, Tampere University; Tampere, Finland

Title of the talk

GPU-friendly Image Restoration 

Short Biography

Karen O. Egiazarian (Eguiazarian) (Fellow IEEE, 2018) received M.Sc. in mathematics from Yerevan State University, Armenia, in 1981, the Ph.D. degree in physics and mathematics from Moscow State University, Russia, in 1986, and a Doctor of Technology from Tampere University of Technology, Finland, in 1994. In 2015, he has received the Honorary Doctoral degree from Don State-Technical University (Rostov-Don, Russia).  Dr. Egiazarian is a co-founder and CEO of Noiseless Imaging Oy (Ltd), Tampere University of Technology spin-off company. He is a Professor at Computational Sciences Unit, Tampere University, Tampere, Finland, leading Computational Imaging group. He was a head of Signal Processing Research Community (SPRC) at TUT, and is a Docent in the Department of Information Technology, University of Jyväskyla, Finland. His main research interests are in the field of computational imaging, compressed sensing, efficient signal processing algorithms, image/video restoration and compression. Dr. Egiazarian has published over 700 refereed journal and conference articles, books and patents in these fields. He is an Editor-in-Chief of Journal of Electronic Imaging (SPIE), Member of the DSP Technical Committee of the IEEE Circuits and Systems Society.

Sergio A. Velastin

Professor, Queen Mary University of London, UK

Title of the talk

From Object Detection to Action Recognition

Short Biography

Prof. Sergio A Velastin is a Visiting Professor at Queen Mary University of London (link: http://mmv.eecs.qmul.ac.uk/). He was previously Conex Research Professor in the Applied Artificial Intelligence Research Group at the Universidad Carlos III in Madrid. He trained and worked most of his life in the UK where he became a Professor in Applied Computer Vision at Kingston University and where he was also director of the Digital Imaging Research Centre. He is also a Fellow of the Institution of Engineering and Technology (IET) and Senior Member of the IEEE where he was member of the Board of Governors of the Intelligent Transportation Society (IEEE-ITSS). Sergio has worked for many years in the field of artificial vision and its application to improve public safety especially in public transport systems. He co-founded Ipsotek Ltd and has worked, on projects with transport authorities in London, Rome, Paris etc in several EU Framework Programme projects.

Wayan Mustika 

Ass. Professor, University Gadjah Mada, Yogyakarta, Indonesia

Title of the talk

Nature-Inspired Optimization Algorithm for Resource Optimization in Heterogeneous Networks

Short Biography

I Wayan Mustika received the B.Eng. degree in electrical engineering from Universitas Gadjah Mada, Indonesia, in 2005, and the M.Eng. degree in computer engineering from King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, in 2008, and the Ph.D. degree in informatics from Kyoto University, Japan, in 2011. He was a Student Activities Advisor of IEEE Indonesia Section in 2014 and Secretary of IEEE Indonesia Section from 2015-2017. In 2017 he was Manager of Research and Publications, Community Services, and Cooperation in Department of Electrical Engineering and Information Technology UGM, Head of Telecommunication and High Frequency System Laboratory, and Smart System and Communication Technology Research Group. Currently, he is Deputy of Publishing and Publication Board for Publication and Printing Affairs (Manager of UGM Press), Universitas Gadjah Mada. His research interests include smart systems, machine-to-machine communications and Internet of Things, and resource management in cognitive radio and heterogeneous networks with a particular emphasis on optimization algorithm. He received the Young Researcher’s Encouragement Award from IEEE VTS Japan in 2010, Student Paper Award from IEEE Kansai Section in 2011, and the Best Paper Award in Communications and Vehicular Technology Track of ICITEE 2015. He is a member of the IEEE.

Aurélio Campilho

Professor, Faculty of Enginnering, University of Porto, Portugal

Title of the talk

Deep Learning in Medical Image Analysis

Short Biography

Deep learning, a machine learning methodology, is a fast advancing technique, achieving impressive levels of performance in medical imaging, in various tasks including the segmentation of the anatomy, the detection and segmentation of lesions, the diagnosis of pathologies and the prediction of disease progression and treatment outcome. Deep learning finds useful applications in many clinical areas, such cardiac image analysis, brain imaging, histopathology image classification, detection of eye diseases or in computer-aided diagnosis of pulmonary pathologies. After a brief overview of the recent advances of deep learning in medical images analysis, we present the fundamentals of deep learning, and describe the advances achieved by the C-BER/Biomedical Imaging Lab from INESC TEC*, using Deep Learning methodologies in ophthalmology and radiology, particularly for the detection and segmentation of eye and lung lesions, where we have reached a competitive performance using Convolutional Neural Networks.
* INESC TEC - The Institute for Systems and Computer Engineering, Technology and Science
C-BER – Centre for Biomedical Engineering Research