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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-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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to </span><span style='font-family:"Calibri",sans-serif'>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-family:"Calibri",sans-serif;color:black'>that cover</span><span style='font-family:"Calibri",sans-serif'>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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>1) Deep Learning. Convolutional Neural Networks<o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>2) Deep Object Detection<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Date/time: </span><span style='font-family:"Calibri",sans-serif'>Wednesday 3<sup>rd</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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Registration can be done using the link: </span></b><b><span style='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-family:"Calibri",sans-serif'>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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Lecture abstract</span></b><b><span style='font-family:"Calibri",sans-serif'>s<o:p></o:p></span></b></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>1)</span></b> <b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>Deep Learning. Convolutional Neural Networks<strong><span style='font-family:"Calibri",sans-serif'>, </span></strong></span></b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>Wednesday 3<sup>rd</sup> June 2020, 17:00-17:45 EEST <o:p></o:p></span></h6><p class=MsoPlainText style='text-align:justify'><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif'>Summary: </span></strong>Introduction to deep learning, focusing on convolutional neural networks (CNNs).<b> </b>From multilayer perceptrons to deep architectures. Fully connected layers. Convolutional layers. Tensors and mathematical CNN formulations. Pooling. Training convolutional NNs. Initialization. Batch Normalization, Data augmentation. Regularization. Dropout. AlexNet, ZFNet, ResNet, SqueezeNet, Inception, GoogleLeNet, Network-In-Network architectures.<b> </b>Lightweight deep learning. <b> </b>Deployment on embedded systems. Performance metrics.<b><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'><o:p></o:p></span></b></p><p class=MsoPlainText style='text-align:justify'><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif'><o:p> </o:p></span></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>2) </span></b><b><span style='font-family:"Calibri",sans-serif;color:windowtext'>Deep Object Detection</span></b><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>,</span></strong><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext;font-weight:normal'> </span></strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>Wednesday 3<sup>rd</sup> June 2020, 17:45-18:30 EEST <o:p></o:p></span></h6><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'>Summary: </span></b><span style='font-family:"Calibri",sans-serif;color:windowtext'>An overview is provided on target detection using deep neural networks. Detection as classification and regression task, Modern architectures for target detection: RCNN, Faster RCNN, R-FCN, YOLO v1/2/3/4, SSD Lightweight detector architectures. Object detection performance metrics. Evaluation and benchmarking. Deployment in embedded platforms.Recently, Convolutional Neural Networks (CNNs) have been used for the task of object detection with great results. However, using such models on drones for real-time face detection is prohibited by the hardware constraints that drones impose. Various architectures and settings are examined to facilitate the use of CNN-based object detectors on a drone with limited computational capabilities.</span><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:windowtext'><o:p></o:p></span></h6><p class=MsoPlainText style='text-align:justify'><span style='font-size:12.0pt;line-height:105%'><o:p> </o:p></span></p><p><b><span style='font-size:12.0pt;color:black'>Lecturer: </span></b><b><span style='font-size:12.0pt'>Prof. Ioannis Pitas </span></b><span style='font-size:12.0pt'>(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-size:12.0pt;color:black'>Lecturing record of Prof. I. Pitas:</span></b><span style='font-size:12.0pt;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-size:12.0pt;color:black'> University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany),</span><span style='font-size:12.0pt;color:black'> National University of Malaysia, </span><span lang=EN-GB style='font-size:12.0pt;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-size:12.0pt'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'>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-family:"Calibri",sans-serif'>General information:</span></b><span style='font-family:"Calibri",sans-serif'> <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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>These two lectures are part of a 1</span><span style='font-family:"Calibri",sans-serif'>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-family:"Calibri",sans-serif;color:black'>Introduction to autonomous systems </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to computer vision </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Image acquisition, camera geometry </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 2<sup>nd</sup> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Stereo and Multiview imaging </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>2<sup>nd </sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Structure from Motion </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>9<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>2D convolution and correlation algorithms</span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>9<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Motion estimation </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>20<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to Machine Learning</span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>20<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'>Artificial Neural Networks. Perceptron <span style='color:black'>(delivered </span>27<sup>th</sup><span style='color:black'> May 2020)</span><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'>Multilayer perceptron. Backpropagation <span style='color:black'>(delivered </span>27<sup>th</sup><span style='color:black'> May 2020)</span><o:p></o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Deep </span></b><b><span style='font-family:"Calibri",sans-serif'>learning.<span style='color:black'> Convolutional NNs<o:p></o:p></span></span></b></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Deep object detection<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Object tracking <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Localization and mapping<o:p></o:p></span></p><p class=MsoNoSpacing><span style='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-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Sincerely yours<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Prof. Ioannis Pitas<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;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-family:"Calibri",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='color:black'>Post scriptum: To stay current on CVMl matters, you may want to register to the CVML email list, following instructions in </span><a href="https://lists.auth.gr/sympa/info/cvml">https://lists.auth.gr/sympa/info/cvml</a></b><o:p></o:p></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-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'><o:p> </o:p></p><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><o:p> </o:p></p></div></div></body></html>