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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Dear Computer Vision/Machine Learning/Autonomous Systems students, engineers, scientists and enthusiasts,<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to launch the live CVML Web lecture series <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>that will cover very important topics Computer vision/machine learning. Two lectures will take place on Saturday 9th May 2020:<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>1) Structure from Motion <o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>2) 2D convolution and correlation algorithms<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Date/time: <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>a) Saturday 11:00-12:30 EET (17:00-18:30 Beijing time) for audience in Asia and will be repeated<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>b) Saturday 20:00-21:30 EET (<em><span style='font-family:"Calibri",sans-serif;font-style:normal'>13:00-14:30</span></em><em><span style='font-family:"Calibri",sans-serif'> </span></em><span class=st>EST, 10:00-11:30 PST for NY/LA, respectively</span>) for audience in the Americas. <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Registration can be done using the link: </span></b><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>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. <o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Lectures abstract<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>1) Structure from Motion <o:p></o:p></span></b></p><p class=MsoPlainText style='text-align:justify'><b><span style='color:black'>Summary: </span></b><span style='color:black'>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. <span class=tadv-color>3D building/monument reconstruction and modeling,</span></span><span class=tadv-color><o:p></o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>2) 2D convolution and correlation algorithms<o:p></o:p></span></b></p><p class=MsoPlainText style='text-align:justify'><b><span style='color:black'>Summary: </span></b><span style='color:black'>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.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Lecturer: Prof. Ioannis Pitas</span></b><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'> (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.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>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). <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: <a href="https://multidrone.eu/">https://multidrone.eu/</a></span><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'> <span style='color:black'>and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative <a href="https://ieeeasi.signalprocessingsociety.org/">https://ieeeasi.signalprocessingsociety.org/</a>.<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Prof. I. Pitas: </span><span class=MsoHyperlink><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el">https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</a></span></span><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>AIIA Lab </span><span class=MsoHyperlink><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><a href="http://www.aiia.csd.auth.gr">www.aiia.csd.auth.gr</a></span></span><span style='font-size:11.0pt;font-family:"Calibri",sans-serif'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>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.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>These two lectures are part of a 14 lecture <b>CVML web course <span class=tadv-color>‘Computer vision and machine learning for autonomous systems’</span></b><span class=tadv-color> (April-June 2020):</span></span><span class=tadv-color><o:p></o:p></span></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Introduction to autonomous systems (delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Introduction to computer vision (delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Image acquisition, camera geometry (delivered 2<sup>nd</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Stereo and Multiview imaging (delivered 2<sup>nd</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>3D object/building/monument reconstruction and modeling <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Signals and systems. 2D convolution/correlation <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Motion estimation <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Introduction to Machine Learning<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Introduction to neural networks, Perceptron, backpropagation<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Deep neural networks, Convolutional NNs<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Deep learning for object/target detection<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Object tracking <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Localization and mapping<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Fast convolution algorithms. CVML programming tools.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Sincerely yours<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Prof. Ioannis Pitas<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'>Director of AIIA Lab, Aristotle University of Thessaloniki, Greece</span><span lang=EL style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:black'><o:p></o:p></span></p></div></body></html>