Dear Computer Vision/Machine Learning/Autonomous Systems students, engineers, scientists and enthusiasts,
Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to launch the live CVML Web lecture series that will cover very important topics Computer vision/machine learning. Two lectures will take place on Saturday 25th April 2020:
1) Introduction to Autonomous Systems
2) Introduction to Computer Vision
Date/time:
a) Saturday 11:00-12:30 EET (17:00-18:30 Beijing time) for audience in Asia and
b) Saturday 20:00-21:30 EET (13:00-14:30 EST, 10:00-11:30 PST for NY/LA, respectively) for audience in the Americas.
Registration can be done using the link: http://icarus.csd.auth.gr/cvml-web-lecture-series/ http://icarus.csd.auth.gr/cvml-web-lecture-series/
Lectures abstract
1) Introduction to Autonomous Systems
Abstract: Mission planning and control, perception and intelligence, embedded computing, swarm systems, communications and societal technologies.
a) autonomous cars, b) drones and drone swarms, c) autonomous underwater vehicles d) autonomous marine vessels and e) autonomous robots.
2) Introduction to Computer Vision
Abstract: image/video sampling, Image and video acquisition, Camera geometry, Stereo and Multiview imaging, Structure from motion, Structure from X, 3D Robot Localization and Mapping, Semantic 3D world mapping, 3D object localization, Multiview object detection and tracking, Object pose estimation.
Lecturer: Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities.
His current interests are in the areas of image/video processing, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 1138 papers, contributed in 50 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 70 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 42 such projects. He has 30000+ citations to his work and h-index 81+ (Google Scholar).
Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/ https://multidrone.eu/ and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/ https://ieeeasi.signalprocessingsociety.org/.
Prof. I. Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el
AIIA Lab http://www.aiia.csd.auth.gr www.aiia.csd.auth.gr
Lectures will consist primarily of live lecture streaming and PPT slides. Attendees (registrants) need no special computer equipment for attending the lecture. They will receive the lecture PDF before each lecture and will have the ability to ask questions real-time. Audience should have basic University-level undergraduate knowledge of any science or engineering department (calculus, probabilities, programming, that are typical e.g., in any ECE, CS, EE undergraduate program). More advanced knowledge (signals and systems, optimization theory, machine learning) is very helpful but nor required.
These two lectures are part of a 14 lecture CVML web course 'Computer vision and machine learning for autonomous systems' (April-June 2020):
1. Introduction to autonomous systems
2. Introduction to computer vision
3. Image acquisition, camera geometry
4. Stereo and Multiview imaging
5. 3D object/building/monument reconstruction and modeling
6. Signals and systems. 2D convolution/correlation
7. Motion estimation
8. Introduction to Machine Learning
9. Introduction to neural networks, Perceptron, backpropagation
10. Deep neural networks, Convolutional NNs
11. Deep learning for object/target detection
12. Object tracking
13. Localization and mapping
14. Fast convolution algorithms. CVML programming tools.
Sincerely yours
Prof. Ioannis Pitas, Director of AIIA Lab, Aristotle University of Thessaloniki, Greece