Connectionists: Last call early registration: Short e-course on Computer Vision and Image Processing, 24-25th February 2021

Mon Feb 1 09:05:52 EST 2021

Dear Computer Vision/Image Processing engineers, scientists and enthusiasts,

you are welcomed to register in this short e-course on 'Computer Vision and
Image Processing', 24-25th February 2021.

It will take place as a two-day e-course (due to COVID-19 circumstances),
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 you to study at own pace.  You
can also self-assess your knowledge, by filling appropriate questionnaires
(one per lecture). You will be provided programming exercises to improve
your programming skills. 

It is part of the very successful CVML short course series that took place
in the last three years. 

Course description 'Computer Vision and Image Processing'

The short e-course consists of 16 1-hour live lectures organized in two
Parts (1 Part per day):

Part A (8 hours)  provide an in-depth presentation of Image Processing
theory and its application in the above-mentioned diverse domains. First, an
Introduction to Image Processing and Computer Vision will be offered to
clarify concepts in a precise and mathematical way. Image formation and its
issues (e.g., image noise, deformations) will then be detailed, whether
based on visible light or on other modalities (e.g., Xrays, Ultrasound).
Image sampling will provide the necessary background to understand the
potential and limitations of digital images.  2D Signals and Systems will
provide the theoretical and algorithmic tools for most image processing
operations. Then notions related to Image transforms will be clarified,
together with their applications in image/video analysis and compression.
Fast 2D convolution algorithms will provide efficient implementation of most
image processing operations. Image perception will overview the Human Visual
System and its impact on image quality and image processing system design
specifications. Finally, Image filtering will provide tools to reduce noise
and enhance image quality, e.g., to increase contrast, perform image zooming
or printing. 

Part B (8 hours) provide fan in-depth presentation of both 2D and 3D
Computer Vision and Image Analysis theory and their applications in the
above-mentioned diverse domains. Edge detection will allow to extract
reliable object contours.  Region segmentation and Texture description will
detail segmentation of an image into homogeneous regions. Either edge or
region object descriptions will be employed in 2D object shape analysis. 3D
Computer Vision starts with a detailed presentation of image acquisition and
camera geometry, including camera calibration. Then, two lectures on a)
Stereo and Multiview imaging and b) Structure from motion will provide the
theoretical and algorithmic tools to recover 3D world models from images.
They will be used on Localization and mapping that is of primary importance
in Autonomous Systems and Robotic perception. Finally, Object tracking is
presented, as it is of primary importance (together with object detection
presented in the ML DNN e-course) in practically all the above-mentioned
Computer Vision applications and way beyond.

Course lectures

Part A Image Processing (first day, 8 lectures):

1.	Introduction to Image Processing and Computer Vision
2.	Image Formation
3.	Image Sampling
4.	2D Systems
5.	Image Transforms
6.	Fast 2D Convolution Algorithms
7.	Image Perception
8.	Image Filtering

Part B Computer Vision  (second day, 8 lectures):

1.	Edge Detection
2.	Region Segmentation. Texture Description
3.	Shape Description
4.	Image Acquisition. Camera Geometry
5.	Stereo and Multiview Imaging
6.	Structure from Motion
7.	3D Robot Localization and Mapping
8.	Object Tracking

Though independent, the attendees of this short e-course will greatly
benefit by attending the CVML short e-course on 'Machine Learning and Deep
Neural Networks' 17-18th February 2021: 

<https://icarus.csd.auth.gr/cvml-short-course-machine-learning-and-deep-neur
al-networks/> CVML Short Course - Machine Learning and Deep Neural Networks

You can use the following link for course registration:

https://icarus.csd.auth.gr/cvml-short-course-computer-vision-image-processin
g/

Lecture topics, sample lecture ppts and videos, self-assessment
questionnaires and programming exercises can be found therein.

For questions, please contact: Ioanna Koroni <koroniioanna at csd.auth.gr
<mailto:koroniioanna at 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). He has 32200+ citations to his work and
h-index 85+.

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
<https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el> &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/

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 in
the CVML email list, following instructions in:
https://lists.auth.gr/sympa/info/cvml

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