RESERVE MY SPOT
Dr. Archontis Giannakidis is a Medical Image Analysis Scientist with the National Heart and Lung Institute (NHLI) of Imperial College London, UK.
He received his BSc in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece, and his MSc (with distinction) in Modern Digital and Wireless Communications from the University of Leeds, UK. In 2010, he was awarded his PhD in Electronic Engineering (area: inverse problems/vector-field tomographic reconstruction) from the University of Surrey, UK.
Dr. Giannakidis has received postdoctoral training in research centres with high global impact (Imperial College London, Berkeley Lab, Johns Hopkins University). He has participated in numerous cutting-edge research projects funded by the European Union (EPSRC, BHF) and the United States of America (NIH, Department of Energy). He has been on the organisation committee for international conferences, and has experience supervising MSc and BSc students. He has won EPSRC scholarship awards due to achieving high academic performance, and is a regular reviewer for major international journals such as IEEE Transactions on Medical Imaging, Journal of Cardiovascular Magnetic Resonance, and IoP.
Archontis possesses teaching experience (private, small groups, and classroom). In the past, he taught Mathematics for 1st year students (small groups) at the Department of Electronic and Electrical Engineering, Imperial College London. He also worked as an IT instructor in Greece, teaching Java, C++.
Dr. Giannakidis's research lies in the area of medical image analysis and machine learning for magnetic resonance imaging (MRI). He has published 30 academic publications.
By the end of this module, students will be able to identify real-world situations where image and video analysis tools can be advantageous. A main objective of this module is to provide an in-depth understanding of the theory and mathematical framework behind fundamental image/video processing tasks. This module will develop the student's ability to utilise state-of-the-art algorithms to perform key image/video processing tasks, such as denoising, recovery, segmentation, classification, and compression. The students will also learn how to evaluate the performance of these techniques. The learner will be able to reflect on the special role of sparsity in modern image and video processing.
SKILLS:
- Medical Imaging
- Diffusion Tensor Imaging
- Signal Processing
- Machine Learning
- Pattern Recognition
- Mathematical Modeling
- Matlab
DATE: 27 Nov - 15 Dec, 2017
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
WHAT YOU WILL LEARN
COURSE OUTLINEABOUT ARCHONTISBIBLIOGRAPHYHARBOUR.SPACE 
IMAGE AND VIDEO ANALYSIS
In this module, you will learn the science behind how images and videos are formed, altered, stored, and used. Digital images and videos are everywhere nowadays in a vast number of applications, such as medicine, biology, robotics (computer vision), surveillance, security, biometrics, TV and entertainment, astronomy, and art, to name a few. The ability to process image and video signals is therefore an incredibly important skill to master for engineering/data science students, software developers, and practicing scientists. Digital image and video processing continues to foster the multimedia technology revolution we are experiencing today. Some important examples of image and video processing include the removal of degradations, the compression and transmission of images and videos for economical storage and efficient transmission. In this module, not only will you learn the theory and mathematical framework behind fundamental processing tasks, including image/video enhancement, recovery, segmentation, encoding and compression, but you will also acquire knowledge on how to perform these key processing tasks in practice using state-of-the-art algorithms and tools. Emphasis on the special role sparsity plays in modern image and video processing will also be given. In all cases, example images and videos pertaining to specific application fields will be used.
ARCHONTIS
GIANNAKIDIS 
HARBOUR.SPACE UNIVERSITY
DATE: 27 Nov – 15 Dec, 2017
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
IMAGE AND
VIDEO ANALYSIS
Session 2
Signals and systems (convolution, correlation, filtering spatial domain, etc.)
Session 3
Discrete 2D Fourier Transform and sampling (frequency domain filtering)
Session 4
Motion estimation and color representation and processing.
Session 1
Introduction (human visual system, image formation, analogue, digital, sampling and quantization, etc.)
All rights reserved. 2017
