Dear Autonomous Systems (cars) engineers, scientists and enthusiasts,

you are welcomed to register in the 'Summer short e-course on Deep Learning
and Computer Vision for Autonomous Systems 2020'  with applications on
autonomous cars, drones and marine vessels.
It will take place on 17-18/8/2020 as an e-course (due to COVID-19
cicrumstances) hosted by the Aristotle University of Thessaloniki (AUTH),
Thessaloniki, Greece, providing a series of live lectures delivered through
a tele-education platform. They will be complemented with on line video
recorded lectures and lecture pdfs, to facilitate international participants
having time difference issues and to enable everybody to study at own pace.

The short e-course consists of 16 1-hour lectures organized in two parts
(one per day):
Part A lectures provide an in-depth presentation to autonomous systems
imaging and the relevant architectures as well as a solid background on the
necessary topics of computer vision (Image acquisition, camera geometry,
Stereo and Multiview imaging, Mapping and Localization) and machine learning
(Introduction to neural networks, Perceptron, backpropagation, Deep neural
networks, Convolutional NNs).
Part B lectures provide in-depth views of the various topics encountered in
autonomous systems perception, ranging from vehicle localization and
mapping, to target detection and tracking, autonomous systems communications
and embedded CPU/GPU CVML computing environments. They also contain
application-oriented lectures on autonomous drones, cars and marine vessels
(e.g. for land/marine surveillance, search&rescue missions,
infrastructure/building inspection and modeling).

You can use the following link for course registration:
http://icarus.csd.auth.gr/dl-and-cv-for-autonomous-cars-2020/
Early registration cutoff date is the 15th  July 2020.

Lecture topics, sample lecture ppts and videos can be found therein.
For questions, please contact: Ioanna Koroni <koroniioanna@csd.auth.gr>

The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow,
Chair of the IEEE SPS Autonomous Systems Initiative, Director of the
Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle
University of Thessaloniki, Greece, Coordinator of the European Horizon2020
R&D project Multidrone. He is ranked 249-top Computer Science and
Electronics scientist internationally by Guide2research (2018). He is head
of the EC funded AI doctoral school of Horizon2020 EU funded R&D project
AI4Media (1 of the 4 in Europe).

AUTH is ranked 153/182 internationally in Computer Science/Engineering,
respectively, in USNews ranking.

Relevant links:
1) Prof. I. Pitas:
https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el
2) Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/
3) Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/
4) Horizon2020 EU funded R&D project AI4Media:  https://ai4media.eu/
5) AIIA Lab: https://aiia.csd.auth.gr/

Course description
Part A (8 hours)
1.           Introduction to autonomous systems imaging
2.           Introduction in computer vision
3.           Image acquisition, camera geometry
4.           Stereo and Multiview imaging
5.           Introduction to neural networks, Perceptron,
6.      Multilayer perceptron. Backpropagation
7.           Deep neural networks. Convolutional NNs
8.           Introduction to multiple drone imaging
Part B (8 hours)
1.            Localization and mapping
2.            Deep learning for object/target detection
3.            Object tracking and 3D localization
4.            CVML software development tools
5.            Fast convolution algorithms
6.            Drone mission planning and control
7.            Introduction to car vision
8.            Introduction to autonomous marine vehicles

Sincerely yours

Prof. I. Pitas
Director of the Artificial Intelligence and Information analysis Lab (AIIA
Lab)
Aristotle University of Thessaloniki, Greece

Post scriptum: To stay current on CVMl matters, you may want to register to
the CVML email list, following instructions in
https://lists.auth.gr/sympa/info/cvml