Date: 17th June 2020
15. Deep Semantic Image Segmentation 19:30-20:15 EEST

16. CVML software development tools 20:15-21:00 EEST

LECTURE DESCRIPTION

LECTURE 15: Deep Semantic Image Segmentation

Wednesday 17th June 2020 , 19:30-20:15 EEST

Summary: Semantic image segmentation is a very important computer vision task with several applications in autonomous systems perception, robotic vision and medical imaging. Recent semantic image segmentation methods rely on deep neural networks and aim to assign a specific class label to each pixel of the input image. This lecture overviews the topic and addresses some of the semantic image segmentation challenges, notably: Deep semantic Image Segmentation architectures. Skip connections. U-nets. BiSeNet. Semantic image segmentation performance, computational complexity and generalization.

Sample lecture material

 – sample slides DOWNLOAD

Related Literature:

  1. I. Pitas, “Digital image processing algorithms and applications”, Wiley 2000.
  2. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062. 2014 Dec 22.

 

Lecture 16: CVML software development tools

Wednesday 17th June 2020 , 20:15-21:00 EEST

SummaryThis lecture overviews the various SW tools, libraries and environments used in computer vision and machine learning: Robotic Operating System (ROS). Libraries (OpenCV, BLAS, cuBLAS, MKL DNN, cuDNN), DNN Frameworks (Neon, Tensorflow, Pytortch, Keras, MXNet), Distributed/cloud computing (MapReduce programming model, Apache Spark), Collaborative SW Development tools (GitHub, Bitbucket).

 

Sample lecture material
 – sample slides 
DOWNLOAD

Related Literature

  1. Bradski G, Kaehler A. Learning OpenCV: Computer vision with the OpenCV library. » O’Reilly Media, Inc.»; 2008 Sep 24.
  2. Kirk DB, Wen-Mei WH. Programming massively parallel processors: a hands-on approach. Morgan kaufmann; 2016.
  3. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M. Tensorflow: A system for large-scale machine learning. In12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16) 2016 (pp. 265-283).

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.

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)

Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/ and is principal investigator (AUTH)  in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/.


Lecturing record of Prof. I. Pitas: 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),  University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, 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.

Relevant links:

http:// https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=en

www.aiia.csd.auth.gr