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Dear Computer Vision, Machine Learning and Autonomous Systems engineers, scientists and enthusiasts,
you are welcomed to register in the ‘Computer Vision, Machine Learning and Autonomous Systems Web Lecture Series’.
After the very successful completion of a) the Spring2020 edition of the CVML Web Lecture Series and b) the ‘Summer School on Autonomous Systems 2020’ 17-21/8/2020, attracting more than 100 registrants, AIIA Lab (AIIA.CVML research group) offers an asynchronous mode to study Computer Vision, Machine Learning and Autonomous Systems topics. CVML Web Lecture list is found below. Sample material of this course is available and lecture topics are provided as well.
This asynchronous e-course provides an overview and in-depth presentation of the various computer vision and deep learning problems encountered in autonomous systems perception, e.g. in drone imaging or autonomous car vision. It consists of 21 one-hour lectures and related material (ppt/pdf, self-assessment exercises, videos, 5 programming exercises), covering the following topics:
- Computer vision. After reviewing image acquisition, camera geometry (mapping the 3D world on a 2D image plane) and camera calibration, stereo and multi-view imaging systems are presented for recovering 3D world geometry from 2D images. This is complemented by Structure from Motion (SfM) towards Simultaneous Localization and Mapping (SLAM) for vehicle and/or target localization and visual object tracking and 3D localization. Motion estimation algorithms are also overviewed.
b. Neural Networks and Deep Learning. As there is much hype and often little accuracy, when treating these topics, first the principles of Machine Learning are presented, focusing on classification and egression. Then, an introduction to neural networks, provides rigorous formulation of the optimization problems for their training, starting with Perceptron. It continues with Multilayer perceptron training through Backpropagation, presenting many related problems, such as over-/under-fitting and generalization. Deep neural networks, notably Convolutional NNs are the core of this domain nowadays and they are overviewed in great detail. Their application on deep learning for object detection is a very important issue as well, complemented with a presentation of deep semantic image segmentation.
c. Autonomous Systems. First of all, an introduction to Autonomous Systems (AS) provides an overview of various issues related to AS perception and control. Then topics related to autonomous drones are detailed, notably drone mission planning and control and multiple drone imaging. Then Autonomous cars and autonomous marine vehicles are overviewed.
d. CVML algorithms and programming. Various such tools, libraries and frameworks are overviewed: Robotic Operating System (ROS), linear algebra libraries (BLAS), DNN libraries (e.g., cuBLAS, cuDNN) and frameworks (e.g., Pytorch, Tensorflow, Keras etc). Distributed computing frameworks (Apache Spark) and collaborative SW development tools are overviewed as well (e.g., GitHub).
e. Signals and Systems. Much confusion exists nowadays in ML literature, as even mature ML scientists have no background on Signals and Systems (SS) and confuse even basic notions, e.g., convolutions and correlations. SS principles are overviewed, while focusing on fast convolution algorithms, particularly on 2D convolution algorithms that are an absolute must for CNN libraries/frameworks and many computer vision tasks.
You can use the following link for course registration:
Lecture topics, sample lecture ppts and videos, self-assessment questionnaires and programming exercises can be found therein.
For questions, please contact: Ioanna Koroni <firstname.lastname@example.org>
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 31600+ citations to his work
and h-index 85+.
AUTH is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews ranking.
1) Prof. I. Pitas:
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/
- Introduction to computer vision
- Image acquisition, camera geometry
- Stereo and Multiview imaging
- Structure from motion
- Localization and mapping
- Object tracking and 3D localization
- Motion estimation
- Introduction to Machine Learning
- Introduction to neural networks, Perceptron
- Multilayer perceptron. Backpropagation
- Deep neural networks. Convolutional NNs
- Deep learning for object detection
- Deep Semantic Image Segmentation
- Introduction to autonomous systems
- Introduction to multiple drone systems
- Drone mission planning and control
- Introduction to car vision
- Introduction to autonomous marine vehicles
CVML algorithms and programming
Signals and Systems
CVML PROGRAMMING EXERCISES
Υou can improve your programming knowledge on Computer Vision, Machine Learning and Image/Video Processing topics through programming exercises using OpenCV, PyTorch and CUDA on the following topics:
- Introduction to OpenCV Programming
- CNN image classification
- PyTorch for deep object detection
- OpenCV programming for object tracking
- CUDA programming of 2D convolution algorithms
You can now assess your CVML knowledge and background by performing the exercise described in:
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 to
the CVML email list, following instructions in