This session has been accepted for the 2021 IEEE ICETCI conference in Hyderabad, at August 25-27, 2021. It is supported by IEEE CIS Neural Network Task Committee.
Artificial Neural Networks have been used in many analysis problems due to their ability to approximate complex functions and relationships following a data-driven learning paradigm. Recent developments in end-to-end learning methodologies based on deep Artificial Neural Network architectures led to remarkable performance improvements in visual information analysis. Nowadays, Multilayer Perceptrons, Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks are the de-facto choices for a variety of problems coming from Computer and Robot Vision fields. However, state-of-the-art ANN-based systems need training on large-scale annotated datasets, and require an enormous number of computations and high memory being available only in specialized computing hardware, like high-end Graphical Processing Units and parallel computational systems. Thus, adoption of such high-performing ANN-based solutions in real-life Computer and Robotic Vision problems in general is limited.
The purpose of the Special Session is to provide a forum to exchange ideas and to discuss developments in Artificial Neural Networks with applications in Computer and Robot Vision. Topics of interest include (but are not limited to):
The Special Session is supported by the H2020 project OpenDR.
Potential authors may submit their manuscripts for presentation consideration through ICETCI 2021 submission system electronically at https://cmswebonline.com/icetci2021 , following the conference guidelines. All submissions will go through peer review.
|Last Date for Paper Submission:||
28 February 2021
|Final Notification of Review Outcomes:||15 May 2021|
|Submission of Camera-Ready Paper and Early-Bird Registration Deadline:||31 May 2021|
Alexandros Iosifidis, Associate Professor, Aarhus University, Aarhus, Denmark, firstname.lastname@example.org
Alexandros Iosiﬁdis received the Diploma (5-year degree) and the M.Sc. degrees (honors) in Electrical and Computer Engineering from Democritus University of Thrace, Greece, in 2008 and 2010, respectively, and the Ph.D. degree (honors) in Informatics from Aristotle University of Thessaloniki, Greece, in 2014. He was a Postdoctoral Researcher in Aristotle University of Thessaloniki and Tampere University of Technology from 2014 to 2017. In 2017, he joined Aarhus University, where he is currently an Associate Professor at the Department of Electrical and Computer Engineering, leading the Machine Learning and Computational Intelligence group.
Dr. Iosifidis is a Senior Member of IEEE and he was the Secretary of the Finnish IEEE Signal Processing/Circuits and Systems Chapter from 2016 to 2018. He was the recipient of the Academy of Finland Postdoctoral Research Fellowship in 2016 and the H. C. Ørsted Forskerspirer prize 2018 for research excellence in signal processing and machine learning. He led/participated in more than 20 research projects financed by EU, Finnish and Danish funding agencies, private foundations, and companies. He is currently serving in the editorial boards of international scientific journals, i.e. as an Associate Editor for Neurocomputing, IEEE Access, and BMC Bioinformatics, and as Area Editor for Signal Processing: Image Communication. He served as Area Chair for the IEEE ICIP 2018, 2019, 2020, and EUSIPCO 2019, 2021, and as Technical Program Committee Chair for IEEE ICASSP 2019. He has (co-)authored 75 journal papers, 95 conference papers, 5 conference abstracts, 4 book chapters and 1 patent in topics of his expertise. His work has already attracted 2500+ citations with h-index 27 (Google Scholar). His research interests include topics of Artificial Neural Networks and Statistical Machine Learning finding applications in Computer/Robot Vision, Financial Engineering, and graph analysis problems.