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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;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;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to </span><span style='font-size:11.0pt'>have <span style='color:black'>launch</span>ed<span style='color:black'> the live CVML Web lecture series <o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>that cover</span><span style='font-size:11.0pt'>s<span style='color:black'> very important Computer </span>V<span style='color:black'>ision/</span>M<span style='color:black'>achine </span>L<span style='color:black'>earning</span> <span style='color:black'>topics. Two </span>new upcoming 45 min <span style='color:black'>lectures will take place </span>soon<span style='color:black'>:<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt'>1) Object tracking<o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt'>2) Mapping and localization<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>Date/time: </span><span style='font-size:11.0pt'>Wednesday 10<sup>th</sup> June 2020, <span style='color:black'> </span>17<span style='color:black'>:00-1</span>8:30<span style='color:black'> EE</span>S<span style='color:black'>T </span>for both lectures (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time).<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;color:black'>Registration can be done using the link: </span></b><b><span style='font-size:11.0pt'><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'>Registration for <span style='color:black'>asynchronous access to CVML live Web lecture material (video, pdf/ppt) </span>for any past/present lecture can be done using the link:<span style='color:black'> </span><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><span style='color:black'><o:p></o:p></span></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt;color:black'>Lecture abstract</span></b><b><span style='font-size:11.0pt'>s<o:p></o:p></span></b></p><h6 style='mso-margin-top-alt:2.0pt;margin-right:0in;margin-bottom:0in;margin-left:0in;margin-bottom:.0001pt;page-break-after:avoid'><b><span style='font-family:"Times New Roman",serif'>1) Object tracking<strong>, </strong></span></b><span style='font-family:"Times New Roman",serif'>Wednesday 10<sup>th</sup> June 2020, 17:00-17:45 EEST <o:p></o:p></span></h6><p class=MsoPlainText style='text-align:justify'><b><span style='font-family:"Times New Roman",serif'>Summary: </span></b><span style='font-family:"Times New Roman",serif'>Object/target tracking is a crucial component of many computer vision systems. Many approaches on face/object tracking in videos will be overviewed, notably based on feature point tracking, or on color/texture target descriptors. Furthermore, this lecture will focus on video tracking methods using correlation filters or convolutional neural networks. Video trackers that are capable of achieving real time performance for long-term tracking on a UAV platform will be overviewed as well.<o:p></o:p></span></p><p class=MsoPlainText style='text-align:justify'><span style='font-family:"Times New Roman",serif'><o:p> </o:p></span></p><h6 style='mso-margin-top-alt:2.0pt;margin-right:0in;margin-bottom:0in;margin-left:0in;margin-bottom:.0001pt;page-break-after:avoid'><b><span style='font-family:"Times New Roman",serif'>2) Mapping and localization<strong>,</strong></span></b><strong><span style='font-family:"Calibri",sans-serif;font-weight:normal'> </span></strong><span style='font-family:"Times New Roman",serif'>Wednesday 10<sup>th</sup> June 2020, 17:45-18:30 EEST <o:p></o:p></span></h6><p class=MsoNormal><b><span style='font-family:"Times New Roman",serif'>Summary: </span></b><span style='font-family:"Times New Roman",serif'>This lecture includes the essential knowledge about how we obtain/get 2D and/or 3D maps that robots/drones need, taking measurements that allow them to perceive their environment with appropriate sensors. Semantic mapping includes how to add semantic annotations to the maps such as POIs, roads and landing sites. The section Localization is exploited to find the 3D drone or target location based on sensors using specifically Simultaneous mapping and localization (SLAM). Finally, the drone localization fusion is presented that improves localization and mapping accuracy by exploiting synergies between different sensor data.<o:p></o:p></span></p><p><b><span style='font-family:"Times New Roman",serif;color:black'>Lecturer: </span></b><b><span style='font-family:"Times New Roman",serif'>Prof. Ioannis Pitas </span></b><span style='font-family:"Times New Roman",serif'>(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). <span style='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='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>.</span><o:p></o:p></span></p><p><b><span style='font-family:"Times New Roman",serif;color:black'>Lecturing record of Prof. I. Pitas:</span></b><span style='font-family:"Times New Roman",serif;color:black'> 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), </span><span lang=EN-GB style='font-family:"Times New Roman",serif;color:black'> University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany),</span><span style='font-family:"Times New Roman",serif;color:black'> National University of Malaysia, </span><span lang=EN-GB style='font-family:"Times New Roman",serif;color:black'>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.</span><span style='font-family:"Times New Roman",serif'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Relevant links: a) Prof. <span style='color:black'>I. Pitas: </span><span class=MsoHyperlink><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el">https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</a></span> b) <span style='color:black'>AIIA Lab </span><span class=MsoHyperlink><a href="http://www.aiia.csd.auth.gr">www.aiia.csd.auth.gr</a></span><span class=MsoHyperlink><span style='color:windowtext;text-decoration:none'><o:p></o:p></span></span></span></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt'>General information:</span></b><span style='font-size:11.0pt'> <span style='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></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>These two lectures are part of a 1</span><span style='font-size:11.0pt'>5<span style='color:black'> 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><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'>Introduction to autonomous systems (delivered 25th April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Introduction to computer vision (delivered 25th April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Image acquisition, camera geometry (delivered 2nd May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Stereo and Multiview imaging (delivered 2nd May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Structure from Motion (delivered 9th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>2D convolution and correlation algorithms (delivered 9th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Motion estimation (delivered 20th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Introduction to Machine Learning (delivered 20th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Artificial Neural Networks. Perceptron (delivered 27th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Multilayer perceptron. Backpropagation (delivered 27th May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Deep learning. Convolutional NNs (delivered 3rd June 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Deep object detection (delivered 3rd June 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt'>Object tracking <o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-size:11.0pt'>Localization and mapping<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Deep Semantic Image Segmentation<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'>Fast convolution algorithms. CVML programming tools.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>Sincerely yours<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>Prof. Ioannis Pitas<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt;color:black'>Director of Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-size:11.0pt'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='font-family:"Times New Roman",serif;color:black'>Post scriptum: To stay current on CVMl matters, you may want to register to the CVML email list, following instructions in </span></b><b><span style='font-family:"Times New Roman",serif'><a href="https://lists.auth.gr/sympa/info/cvml">https://lists.auth.gr/sympa/info/cvml</a></span></b><span style='font-family:"Times New Roman",serif'><o:p></o:p></span></p><div id=DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><span style='font-family:"Times New Roman",serif'><o:p> </o:p></span></p></div></div></body></html>