CVML live Web lectures 9th May 2020: 1) Structure from Motion 2) 2D convolution and correlation algorithms

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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 9th May 2020:

1) Structure from Motion 

2) 2D convolution and correlation algorithms


  1. a) Saturday 11:00-12:30 EET (17:00-18:30 Beijing time) for audience in Asia and will be repeated
  2. 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:

From this week onwards, asynchronous access to past CVML live Web lecture material (video, pdf/ppt) will be allowed. Separate email will be sent for this option.

Lectures abstract

1) Structure from Motion 

Summary: Image-based 3D Shape Reconstruction, Stereo and multiview imaging principles. Feature extraction and matching. Triangulation and Bundle Adjustment. Mathematics of structure from motion. UAV image capturing. Optimal UAV flight trajectory/flight height/viewing angle/image overlap ratio. Pre/post-processing for 3D reconstruction: flat surface smoothing/mesh modification/isolated point removal.  Structure from motion applications: 3D face reconstruction from uncalibrated video. 3D landscape reconstruction. 3D building/monument reconstruction and modeling,


2) 2D convolution and correlation algorithms

Summary: 2D convolutions play an extremely important role in machine learning, as they form the first layers of Convolutional Neural Networks (CNNs). They are also very important for computer vision (template matching through correlation, correlation trackers) and in image processing (image filtering/denoising/restoration). 3D convolutions are very important for machine learning (video analysis through CNNs) and for video filtering/denoising/restoration. 1D convolutions are extensively used in digital signal processing (filtering/denoising)  and analysis (also through CNNs). Therefore, 2D convolution and correlation algorithms are very important both for machine learning and for signal/image/video processing and analysis. As their computational complexity is of the order O(N^4), their fast execution is a must. This lecture will overview 1D/2D linear and cyclic convolution. Then it will present their fast execution through FFTs, resulting in algorithms having computational complexity of the order O(Nlog2N), O(N^2log2N) for 1D and 2D convolutions respectively. Parallel block-based 2D convolution/calculation methods will be overviewed.  The use of 2D convolutions in Convolutional Neural Networks will be presented.

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: and is principal investigator (AUTH)  in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative

Prof. I. Pitas:


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                                                             (delivered   2nd May 2020)

Stereo and Multiview imaging                                                                       (delivered   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