[visionlist] CVML live Web lectures 2nd May 2020: 1) Image acquisition, Camera geometry 2) Stereo and Multiview imaging

[ioannakoroni at csd.auth.gr]

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 2nd May 2020:

 

1) Image acquisition, Camera geometry 

2) Stereo and Multiview imaging

 

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/

 

Lectures abstract

1) Image acquisition, Camera geometry 

Abstract: After a brief introduction to image acquisition and light
reflection, the building blocks of modern cameras will be surveyed, along
with geometric camera modeling. Several camera models, like the pinhole and
the weak-perspective camera model, will subsequently be presented.
Projective geometry will be overviewed, with the most commonly used camera
calibration techniques closing the lecture.

 

2) Stereo and Multiview imaging

Abstract: Stereoscopic and multiview imaging will be explored in depth. The
fundamentals of stereopsis will be overviewed.  Stereoscopic vision,
geometry will be presented, focusing on epipolar geometry,
fundamental/essential matrix and camera rectification. Stereo camera
technologies  will be overviewed. Subsequently, the main methods of 3D scene
reconstruction from stereoscopic video will be described based on feature
detection and matching. Classical and neural disparity estimation methods
will be presented. 3D depth estimation in parallel, converging and arbitrary
camera geometries will be also presented, along with the basics of multiview
imaging.

 

 

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
(scheduled   2nd May 2020)

Stereo and Multiview imaging
(scheduled   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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