-------- Forwarded Message --------
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 9th May 2020:
1) Structure from Motion
2) 2D convolution and correlation algorithms
Date/time:
a) Saturday 11:00-12:30 EET (17:00-18:30 Beijing time) for
audience in Asia
and will be repeated
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/
From this week onwards, asynchronous
access to past CVML live Web lecture
material (video, pdf/ppt) will be allowed. Separate email will be
sent for
this option.
Lectures abstract
1) Structure from Motion
Summary: Image-based 3D Shape Reconstruction, Stereo and multiview
imaging
principles. Feature extraction and matching. Triangulation and
Bundle
Adjustment. Mathematics of structure from motion. UAV image
capturing.
Optimal UAV flight trajectory/flight height/viewing angle/image
overlap
ratio. Pre/post-processing for 3D reconstruction: flat surface
smoothing/mesh modification/isolated point removal. Structure from
motion
applications: 3D face reconstruction from uncalibrated video. 3D
landscape
reconstruction. 3D building/monument reconstruction and modeling,
2) 2D convolution and correlation algorithms
Summary: 2D convolutions play an extremely important role in
machine
learning, as they form the first layers of Convolutional Neural
Networks
(CNNs). They are also very important for computer vision (template
matching
through correlation, correlation trackers) and in image processing
(image
filtering/denoising/restoration). 3D convolutions are very
important for
machine learning (video analysis through CNNs) and for video
filtering/denoising/restoration. 1D convolutions are extensively
used in
digital signal processing (filtering/denoising) and analysis (also
through
CNNs). Therefore, 2D convolution and correlation algorithms are
very
important both for machine learning and for signal/image/video
processing
and analysis. As their computational complexity is of the order
O(N^4),
their fast execution is a must. This lecture will overview 1D/2D
linear and
cyclic convolution. Then it will present their fast execution
through FFTs,
resulting in algorithms having computational complexity of the
order
O(Nlog2N), O(N^2log2N) for 1D and 2D convolutions respectively.
Parallel
block-based 2D convolution/calculation methods will be overviewed.
The use
of 2D convolutions in Convolutional Neural Networks will be
presented.
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/ and is principal investigator (AUTH) in
H2020
projects Aerial Core and AI4Media. He is chair of the Autonomous
Systems
initiative
https://ieeeasi.signalprocessingsociety.org/.
Prof. I. Pitas:
https://scholar.google.gr/citations?user=lWmGADwAAAAJ
<https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el>
&hl=el
AIIA Lab
www.aiia.csd.auth.gr <http://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):
Introduction to autonomous systems
(delivered 25th April 2020)
Introduction to computer vision
(delivered 25th April 2020)
Image acquisition, camera geometry
(delivered 2nd May 2020)
Stereo and Multiview imaging
(delivered 2nd May 2020)
3D object/building/monument reconstruction and modeling
Signals and systems. 2D convolution/correlation
Motion estimation
Introduction to Machine Learning
Introduction to neural networks, Perceptron, backpropagation
Deep neural networks, Convolutional NNs
Deep learning for object/target detection
Object tracking
Localization and mapping
Fast convolution algorithms. CVML programming tools.
Sincerely yours
Prof. Ioannis Pitas
Director of AIIA Lab, Aristotle University of Thessaloniki, Greece