The course will take place as live web course.

DESCRIPTION

Nowadays, digital images and video are everywhere. Image Processing and Analysis revolutionizes very many domains, notably:

  • Digital Media (video/image/movie) Content Production and Broadcasting, Social Media Analytics,
  • Medical/Biological/Dental Imaging and Diagnosis,
  • Big Visual Data Analytics,
  • Internet and Communications (media broadcasting, streaming).
  • Scientific Imaging of any sort, e.g., Remote Sensing, Environment Sensing.

Furthermore, our dream to make machines sense and perceive (notably see) comes true: Nowadays Computer Vision enables diverse applications:

  • Autonomous Systems (cars, drones, vessels) Perception,
  • Robotics Perception and Control,
  • Intelligent Human-Machine Interaction,
  • Anthropocentric (human-centered) Computing,
  • Smart Cities/Buildings and Assisted living.

Visual Computing, encompassing Computer Vision and Image Processing, coupled with AI (notably Machine Learning and Deep Neural Network) advances hit the news almost every day.

This two-day short course focuses on Computer Vision and Image Processing theory, their applications in the above-mentioned diverse domains and new challenges ahead. It consists of two parts (A, B) and each of them including 8 one-hour lectures and related material (slide pdfs, lecture videos, understanding questionnaires).

Part A lectures (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 lectures (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.

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: 
CVML Short Course – Machine Learning and Deep Neural Networks

UNIVERSITY AUTH

This short course is hosted by Aristotle University of Thessaloniki (AUTH). It is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews and is the biggest University in SE Europe.

WHEN?

The course will take place as a live web course on 24-25 February 2021.

PROGRAM

You can click on the lecture title to view its description.

Time*/date 24/02/2021 25/02/2021
08:00 – 09:00 Registration  
09:00 – 10:00 Introduction to Image Processing and Computer Vision Edge Detection
10:00 – 11:00 Image Formation Region Segmentation. Texture Description
11:00 – 11:30 Coffee break Coffee break
11:30 – 12:30 Image Sampling Shape Description
12:30 – 13:30 2D Systems Image Acquisition. Camera Geometry
13:30 – 14:30 Lunch break Lunch break
14:30 – 15:30 Image Transforms Stereo and Multiview Imaging
15:30 – 16:30 Fast 2D Convolution Algorithms Structure from Motion
16:30 – 17:00
Coffee break
Coffee break
17:00 – 18:00
Image Perception 3D Robot Localization and Mapping
18:00 – 19:00
Image Filtering Object Tracking

*Eastern European Time (EET)

**This programme is indicative and may be modified without prior notice by announcing (hopefully small) changes in lectures/lecturers.

***Each topic will include a 45-minute lecture and a 15-minute break.

 

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 (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.

His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 1000 papers, contributed in 47 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. Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/. He is AUTH principal investigator in H2020 R&D projects Aerial Core and AI4Media. He is chair of the Autonomous Systems Initiative https://ieeeasi.signalprocessingsociety.org/. 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+ (Google Scholar)

Prof. Pitas lead the big European H2020 R&D project MULTIDRONE and is principal investigator (AUTH)  in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/.

Professor Pitas will deliver 16 lectures on deep learning and computer vision.

Educational record of Prof. I. Pitas: He was Visiting/Adjunct/Honorary Professor/Researcher and lectured at several Universities: University of Toronto (Canada), University of British Columbia (Canada), EPFL (Switzerland), Chinese Academy of Sciences (China),  University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, Henan University (China). He delivered 90 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He run 17 short courses and tutorials on Autonomous Systems, Computer Vision and Machine Learning, most of them in the past 3 years in many countries, e.g., USA, UK, Italy, Finland, Greece, Australia, N. Zealand, Korea, Taiwan, Sri Lanka, Bhutan.

https://aiia.csd.auth.gr/computer-vision-machine-learning/#pitas

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

 

REGISTRATION

Early registration (till 15/02/2021):

• Standard: 300 Euros

• Undergraduate/MSc/PhD student*: 200 Euros

Later or on-site registration (after 15/02/2021):

• Standard: 350 Euros

• Undergraduate/MSc/PhD student*: 250 Euros

*Proof of student status should be provided upon registration.

Up to 10 PhD students, registered in AUTH or in any VISION CSA https://www.vision4ai.eu or AI4Media https://ai4media.eu/  or HumanAI-E Net https://www.humane-ai.eu/  University partners, are entitled for 1 free CVML Web Course registration per fall/spring semester on a FCFS basis, with priority to ones working on AI-related topics. This offer is related to the upcoming educational activities of International AI Doctoral Academy (AIDA) http://www.i-aida.org/ that is co-initiated by these two projects.

Register!!!

After the completion of your payment, please fill in the form below:

Complete your registration

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Lectures will be in English. PDF slides will be available to course attendees.

A certificate of attendance will be provided.

***The short course on «CVML Short Course – Computer Vision and Image Processing» will take place as live web course on 17-18 February 2021. Lectures will be prerecorded to facilitate attendees in case they experience problems due to time difference. Remote participation will be available via teleconferencing.***

Cancelation policy:

  • 50% refund for cancelation up to 31/01/2021
  • 0% refund afterwards

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KNOWLEDGE SELF-ASSESSMENT

You may want to self-assess your knowledge on Computer Vision and Image Processing topics, by trying the assessment exercises in  https://aiia.csd.auth.gr/cvml-knowledge-self-assessment/ before and after studying this course.

It takes you only 15 min per questionnaire. You can do this double self-assessment (before/after study) for free, using the sample lecture study material provided below.

TOPICS

24th February 2021 – Part A: (first day, 8 lectures)
Image Processing

Introduction to Image Processing and Computer Vision
Image Formation
Image Sampling
2D Systems
Image Transforms
Fast 2D Convolution Algorithms
Image Perception
Image Filtering

25th February 2021 – Part B: (second day, 8 lectures)
Computer Vision

Edge Detection
Region Segmentation. Texture Description
Shape Description
Image Acquisition. Camera Geometry
Stereo and Multiview Imaging
Structure from Motion
3D Robot Localization and Mapping
Object Tracking

 

AUDIENCE

Any engineer or scientist practicing or student having some knowledge of Signals and Systems, and Machine Learning, notable CS, CSE, ECE, EE students, graduates or industry professionals with relevant background.

IF I HAVE A QUESTION?

Contact

 

PAST COURSE EDITIONS

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Fall School 2020

Participants: 35

Countries: France, Taiwan, United States, Sweden, Denmark, Germany, Pittsburgh, Germany, Greece, England, Italy, Spain, Iceland.

Registrants comments:

  • «The quality of the lectures was high; the professor did a very good job explaining clearly all the concepts. It went according to the plan, and to my personal expectations.»
  • «Personal experience is always important in this kind of courses. Sometimes adds more gain than general theory.»
  • «It was an intensive ride through a wide range of topics regarding drones in research, which I enjoyed quite a lot. It was helpful to brush on past knowledge and accumulate new one. The course also offered me the possibility to encounter a community of passionate researchers which can only motivate me to work harder.»

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Summer School 2020 (held as a web course due to Covid-19 circumstances)

Countries: Belgium, Ireland, Greece, Finland, China, Italy, France, Croatia, Spain, United Kingdom

Registrants comments:

  • «Very interesting lecture topics regarding the autonomous systems perception.»
  • «The overall course was quite interesting and fulfilling in terms of the context promised.»
  • «The lectures were very appealing and satisfactorily delivered.»

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Summer School 2019

Participants: 39

Countries: United Kingdom, Scotland, Germany, Italy, Norway, Slovakia, Spain, Croatia, Czech Republic, Greece

Registrants comments: 

  • «Very adequate information about the topics of DL, CV and autonomous systems.»
  • «Very good coverage of autonomous systems vision perception.»
  • «Course’s content was greatly explanatory with many application examples.»
  • «Very well structured course, knowledgeable lecturers.»

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SAMPLE COURSE MATERIAL & RELATED LITERATURE

  1. Pitas, 3D imaging science and technologies, Amazon CreateSpace preprint, 2021.
  2. Pitas, Computer Vision and Machine Learning, Amazon CreateSpace preprint, 2021.
  3. Pitas, Digital video processing and analysis, China Machine Press, 2017 (in Chinese).
  4. Pitas, Graph analysis in social media, editor, CRC Press, 2016.
  5. Pitas, Digital Video and Television, Createspace/Amazon, 2013.
  6. Pitas, Digital Image Processing Algorithms and Applications, J. Wiley, 2000.
  7. Nikolaidis and I. Pitas, 3D Image Processing Algorithms, J. Wiley, 2000.

 

USEFUL LINKS

•Prof. Ioannis Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el

•Department of Computer Science, Aristotle University of Thessaloniki (AUTH): https://www.csd.auth.gr/en/

Laboratory of Artificial Intelligence and  Information Analysis: http://www.aiia.csd.auth.gr/

•Thessaloniki: https://wikitravel.org/en/Thessaloniki