[visionlist] CVML live Web lectures 9th May 2020: 1) Structure from Motion 2) 2D convolution and correlation algorithms

ioannakoroni at csd.auth.gr ioannakoroni at csd.auth.gr
Mon May 4 06:37:04 -04 2020

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



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:

>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



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

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