(Lecture batch is part of the CVML web course ‘Computer vision and machine learning for autonomous systems’, to be delivered April-June 2020).
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.
His current interests are in the areas of machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 860 papers, contributed in 44 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 69 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 41 such projects.
He has 31000+ citations to his work and h-index 83+ (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/.
Lecturing 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.
ASYNCHRONOUS WEB LECTURE REGISTRATION
You can now also register to access past Lecture material: a) lecture ppt/pdf, b) recorded video, c) lecture understanding questionnaire
c) lecture evaluation form.
If you are interested of multiple past Lectures you need to change the «quantity» option accordingly and let us know about which ones.
Separate registration is needed το access each lecture batch (two 45 min lectures).
Registration fee (per lecture batch): 15 Euros
After you have registered you will receive the all corresponding material of it. .
Cancellation: If a 2-lecture batch is cancelled at lecturer’s fault, full reimbursement will be made to registrants. No reimbursement is allowed, in case a registrant decides not to attend.
Fee Policy: Lecture Batch registration fees may change without warning for future lectures.
CVML WEB LECTURE SERIES CONCEPT
Computer Vision and Machine Learning (CVML) Live Web Lecture Series
Artificial Intelligence and Information analysis (AIIA) Lab, AUTH is proud to launch the live CVML Web lecture series that will cover very important topics Computer vision/machine learning. Lectures will be delivered by Prof. Ioannis Pitas (Head of AIIA Lab, IEEE Fellow, IEEE Distinguished speaker, EURASIP fellow), aiming at providing in-depth knowledge on various CVML topics. Also top scientists internationally may occasionally deliver some lectures, A lecture batch (two consecutive 45 min lectures) will take place on Wednesdays, to avoid conflicts with other intended registrant schedules/duties:
Wednesdays 17:00-18:30 EEST (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time).
(Lectures 15 and 16 will be delivered at 19:30-21:00 EEST on June 17th)
Each lecture batch will be announced at least 1 week in advance in the CVML web lecture series www page (this one) and also in various relevant email lists. Each lecture will be self-sufficient and may be attended by registrants independently from other lectures, if so desired. Lectures will be selected and announced from the attached ‘Tentative CVML Web Lecture List’ (see below), which will be steadily enriched with new lecture topics. Of course, regular participation in the CVML web lecture series will facilitate understanding the entire CVML domain and its subdomains.
Due to multiple requests (as a result of different time zones), asynchronous Web Lecture Series is now foreseen: You can now register to access and study at your pace previous lecture batch material:
a) lecture ppt/pdf,
b) recorded video,
c) lecture understanding questionnaire
d) lecture evaluation form.
Questions on lecture material can also be sent off-line to email@example.com. All other CVML web lecture series rules apply also to the asynchronous mode.
Lectures will be selected and presented in such a way so that 14 consecutive lectures (7 lecture batches) form a CVML web course, roughly corresponding to one semester senior undergraduate or graduate ECE/CS/EE course. The CVML web course Computer vision and machine learning for autonomous systems’ (see below) will be delivered on Wednesdays April-June 2020.
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.
The CVML web lecture series content, lecture timing and exact lecture topics may vary from the above ones depending on intended audience interest and lecturer availability.
The CVML web lecture series is expected to last till 30th August 2020 (end of the academic year 2019-2020). It will contain max 42 lectures (max 3×13 weeks), organized in batches of 2 lectures per week (lasting 1 1/2 consecutive hours per batch). A new series will start on September 2020 for the academic year 2020-2021.
CVML WEB COURSE
‘Computer vision and machine learning for autonomous systems’
16 lectures. One lecture batch (two 45 min lectures) each Wednesday 17:00-18:30 EEST (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time).
(Lectures 15 and 16 will be delivered at 19:30-21:00 EEST on June 17th)
- 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)
- Structure from Motion (delivered 9th May 2020)
- 2D convolution and correlation (delivered 9th May 2020)
- Motion estimation (delivered 20th May 2020)
- Introduction to Machine Learning (delivered 20th May 2020)
- Artificial Neural Networks, Perceptron (delivered 27th May 2020)
- Multilayer perceptron, Backpropagation (delivered 27th May 2020)
- Deep Learning. Convolutional Neural Networks (delivered 3rd June 2020)
- Deep Object Detection (delivered3rd June 2020)
- Object tracking (delivered 10th June 2020)
- Localization and mapping (delivered 10th June 2020)
- Deep Semantic Image Segmentation (delivered 17th June 2020)
- CVML software development tools (delivered 17th June 2020)
Lectures 5,6: Structure from Motion | 2D convolution and correlation
Lecture 7,8: Motion Estimation | Introduction to Machine Learning
Lecture 11,12: Convolutional Neural Networks | Deep Object Detection
Lecture 13,14: Object Tracking | Localization and Mapping
ATTENDANCE AND CERTIFICATES
After the registration cutoff date/time, you will receive a link on how to attend the web lecture,
a) lecture PDF
b) lecture questionnaire
c) lecture evaluation form.
Each lecture has a) a main lecturer, b) a tutor and c) administrator.
Lecture language will be English.
The tool ‘Skype for Business’ will be used for web lecturing (PDF+live lecturer video+ questions using chat/audio).
It is strongly recommended you join the lecture 10 min before its formal start, to have an informal chat with the lecturer and other attendants on general topics and have the feeling of a real live class.
During lecture, you can ask questions any time by chat to be replied by the lecture tutor, or afterwards by email, in case they are many. Live audio questions will be allowed in the middle and the end of the lecture (around min 25 and min 40). Live discussion on general topics will follow for another 10 min after the formal end of the lecture batch.
If you want to receive a certificate of lecture attendance with mark (in the range 0-10, 10 being the best mark, 5 being the pass mark), you have to submit to the lecture administrator (firstname.lastname@example.org) within 48 hours after formal lecture end:
a) your replies to lecture questionnaire: very short replies (1-2 text lines) to each of 10 questions.
You should have no problem replying them within 5-10 min, if you understood the lecture topics
b) your filled lecture evaluation form.
If Asynchronous Web Lecture Series is chosen, you can now register to access and study the lectures at your own pace. Questions on lecture material can also be sent off-line to email@example.com.
If somebody attends at least 12 of the 14 lectures of a CVML web course and delivers (a) above for all of them, she/he can get a certificate of attendance of each course mark (in the range 0-10, 10 being the best mark, 5 being the pass mark).
Registrants to each 14-lectures CVML web-course can be credited 2 ECTS.
TENTATIVE CVML WEB LECTURES LIST (150 lectures)
Mathematical foundations of CV and ML
- Mathematical Analysis
- Linear Algebra
- Set theory
- Probability Theory
- GPU and multicore CPU architectures and computing
- CVML software development tools
- Introduction to computer vision
- Digital images and videos
- Image acquisition, camera geometry
- Computational optics
- Stereo vision
- Stereo and Multiview imaging
- Structure from Motion
- Shape from X
- Active and passive 3D reconstruction methods
- 3D Shape Representations
- 3D Robot Localization and Mapping
- Semantic 3D world mapping
- 3D object localization
- Multiview object detection and tracking
- Shot types in cinematography
- Object pose estimation
- Introduction to Machine Learning
- Data Clustering
- Distance based classification
- Bayesian Learning
- Parameter estimation
- Dimensionality reduction
- Graph-based Dimensionality reduction
- Semi-supervised learning/Label propogation
- Decision surfaces. Support Vector Machines
- Kernel methods
- Syntactic pattern recognition
- Artificial Neural Networks. Perceptron
- Multilayer perceptron. Backpropagation.
- Convolutional Neural Networks
- Spiking Neural Networks
- Recurrent Neural Networks
- Deep Reinforcement Learning
- Object Detection
- Advanced Object Detection
- Few shot object recognition
- Deep Autoencoders
- Deep Semantic Image Segmentation
- Generative Adversarial Networks
- Synthetic map generation
- DNN privacy protection
- ML for image indexing and retrieval
- Deep segmentation
- Introduction to autonomous systems
- Introduction to ROS
- Autonomous systems sensors
- Drone and robot swarms
- Privacy protection, ethics and regulations in AS
- 5G/IoT for autonomous systems communications
- Introduction to autonomous car vision
- 3D Road Surface Reconstruction
- Road condition assessment
- Street environment perception
- Road traffic monitoring
- Autonomous car control
- Introduction to UAV multicopters
- Multiple drone mission planning and control
- Multiple drone Imaging for media production
- Drone cinematography
- Drone HCI
- Multiple drone system architecture
- Multiple Drone Communications
- Cinematography Issues in sports filming
- Imaging for Drone Safety
- Drone regulatory issues
- Drone mission simulations
- Multiple Drone media production
- Multidrone Datasets
- UAV infrastructure inspection
Autonomous Marine Systems
- Autonomous marine surface vessels
- Autonomous underwater vessels
Signal and systems
- Fast convolution algorithms
- Introduction to Image Processing
- Image sampling
- 2D systems
- 2D Digital Filter Design and Realization
- Fast 2D convolution algorithms
- Image transforms
- Digital Image Formation
- Image Perception
- Color Theory
- Image quality
- Computational aesthetics
- Digital Image Filtering
- Image compression
- Edge detection
- Region segmentation
- Image Features
- Shape description
- Mathematical Morphology
3D Image processing
- Introduction to 3D Image Processing
- Medical image acquisition
- 3D Image and Video Quality
- 3D Data Processing
- 3D Image and Shape Compression
- 3D Video Coding And Broadcasting
- 3D Image and Video Analysis
- Image/volume registration
- Image Rendering and View Synthesis
- 3D Video Content Description
- 3D Display Technologies
- Immersion in Virtual Reality
- Introduction to Video Processing
- Fast 3D convolutions for deep video analysis
- Video Digitization
- Moving Image Perception
- Video Filtering
- Video streaming
- Motion Estimation
- 2D visual object tracking
- Joint Detection and Tracking
- Τransform video compression
- Video indexing and retrieval
- Video Description
- 2D/3D Video Production
Human centered computing
- Introduction to human centered computing
- Face/Head Detection
- Person Detection. Pedestrian detection.
- Crowd detection
- Face Recognition
- Face Verification and clustering
- Facial expression recognition
- Human Action Recognition
- Gesture recognition
- Ηuman body pose estimation
- Facial Features Detection
- Visual speech recognition
- Sports video analysis
- Athlete motion analysis
- Anthropocentric video description schemes
Social media analysis
- Graphs in social and digital media
- Algebraic graph analysis
- Web search based on ranking
- Information diffusion
- Graph signal processing
- Matrix and Tensor Factorization for Recommendation Systems
- Big Data Analytics for Social Networks
- Big Graph Storage, Processing and Visualization
- Prof. I. Pitas Google scholar: https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el
- AERIAL-CORE R&D Project (AERIAL COgnitive integrated multi-task Robotic system with Extended operation range and safety) funded by the EU (2019-2), within the scope of the H2020 framework. URL: https://aerial-core.eu/
- Multidrone R&D Project (MULTIple DRONE platform for media production), funded by the EU (2017-19), within the scope of the H2020 framework. URL: https://multidrone.eu/
- Artificial Intelligence and Information analysis (AIIA) Lab www.aiia.csd.auth.gr