posted Aug 2, 2021, 6:17 PM by SangHyun Seo
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updated Aug 23, 2021, 12:18 AM by Sang-Soo Yeo
]
Dr. Irfan Mehmood
Assistant Professor, School of Media, Desing and Technology,
University of Bradford, UK
Irfan
Mehmood has been involved in IT industry and academia in Pakistan, South Korea,
and UK for over 10 years. He is now serving as a Assistant Professor in Applied
Artificial Intelligence, Faculty of Engineering & Informatics, School of
Media, Design and Technology, University of Bradford, UK. His sustained
contribution at various research and industry-collaborative projects give him
an extra edge to meet the current challenges faced in the field of multimedia
analytics, information mining and summarization. Specifically, he has made
significant contribution in the areas of visual surveillance, information
mining and data encryption. He has published 90+ papers in peer-reviewed
international journals and conferences such as Information Fusion,
Neurocomputing, IEEE Access, IEEE Transactions on Industrial Informatics, IEEE
Internet of Things Journal, International Journal of Information Management,
Future Generation Computer Systems, Sensors, Journal of Visual Communication
and Image Representation, Multimedia Tools and Applications, Computers in Human
Behaviour, EURASIP Journal on Image and Video Processing, Mobile Networks and
Applications, Computers in Biology and Medicine, Journal of Medical Systems,
Signal, Image and Video Processing, Bio-Medical Materials and Engineering, KSII
Transactions on Internet and Information Systems, NBIS 2015, MITA 2015, PlatCon
2016, SKIMA 2019, and IWFCV 2020. He is serving as a professional reviewer for
numerous well-reputed journals such as Journal of Visual Communication and
Image Representation, Future Generation Computer Systems, IEEE Access, Journal
of Super Computing, Signal Image and Video Processing, Multimedia Tools and
Applications, ACM Transactions on Embedded Computing Systems, and Enterprise
Information Systems. He acted as GE/LGE in several special issues of SCI/SCIE
indexed journals and is currently involved in editing of several other special
issues.
Abstract of Irfan Mehmood's Talk
In recent years, there has been a
tremendous increase in video capturing devices, which led to large personal and
corporate digital video archives. This huge volume of video data became a
source of inspiration for the development of vast numbers of applications such
as visual surveillance, multimedia recommender systems, and context-aware
advertising. The heterogeneity of video data, higher storage, processing cost,
and communication requirements demand for a system that can efficiently manage
and store huge amount of video data, while providing user-friendly access to
stored data at the same time.
To address this problem, video
summarization schemes have been proposed. Video summarization refers to the
extraction of keyframes, identifying most important and pertinent content. For
instance, gastroenterologist uses wireless capsule endoscopy video technology
to diagnose his patients. However, during capsule endoscopy process, video data
are produced in huge amounts, but only a limited amount of data is actually
useful for diagnosis. In this talk, we will explore two different aspects of
video summarization: visual surveillance and medical imaging.
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posted Aug 2, 2021, 6:12 PM by SangHyun Seo
[
updated Aug 23, 2021, 11:44 PM by Sang-Soo Yeo
]
Dr. Feng Tian
Professor, Bournemouth
University, UK
Dr Feng Tian is currently a professor in Bournemouth University, UK. With expertise
on digital media, image processing and machine learning, Dr Tian has published
over 100 papers or book chapters in peer-reviewed journals or international
conferences, including IEEE Transactions on
Visualization and Computer Graphics, ACM Transactions on Modelling
and Computer Simulation, IEEE Transactions on Cybernetics,
Visual Computer, Computer & Graphics, Multimedia Tools & Applications,
International Joint Conference on Artificial Intelligence (IJCAI), Association
for the Advancement of Artificial Intelligence (AAAI), Pacific Graphics (PG),
IJCNN, CASA, CGI, etc. Before coming to the UK, Dr Tian worked as a
post-doctoral fellow and assistant professor in Nanyang Technological
University, Singapore. Dr Tian has also been awarded with research grants from
Singapore National Research Foundation (Singapore), Royal Society (UK), British
Art Council (UK), Horizon 2020 (EU), etc.
Abstract of Feng Tian's Talk
A good data representation can typically reveal the latent structure of
data and facilitate further processes such as clustering, classification and
recognition. Nonnegative matrix factorization (NMF) as a fundamental approach
for data representation has attracted great attentions. Despite its great
performance, traditional NMF fails to explore the semantic information of
multiple components as well as the diversity among them, which would be of
great benefit to understand data comprehensively and in depth. In fact, real
data are usually complex and contain various components. For example, face
images have ex-pressions and genders. Each component mainly reflects one aspect
of data and provides information others do not have. In this talk, I will
present an approach on multi-component nonnegative matrix factorization
(MCNMF). Instead of seeking only one representation of data, MCNMF learns
multiple representations simultaneously, where each representation corresponds
to a component. By integrating the multiple representations, a more
comprehensive representation is then established.
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