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Seminar Advanced Topics in Visual Computing and Visualisation

Seminar Advanced Topics in Visual Computing and Visualisation
Typ: Seminar (S)
Semester: SS 2018
Zeit: 19.04.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten


26.04.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

03.05.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

17.05.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

24.05.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

07.06.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

14.06.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

21.06.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

28.06.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

05.07.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

12.07.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten

19.07.2018
14:00 - 15:30 wöchentlich
50.34 Raum 131 50.34 INFORMATIK, Kollegiengebäude am Fasanengarten


Dozent:

Prof. Dr.-Ing. Boris Neubert

M.Sc. Chengzhi Wu

SWS: 2
LVNr.: 2400077

 

Kurzbeschreibung

Topics for this year's seminar include computational photography, deep-learning-based 2D image processing and 3D shape learning which can bedefined as follows:

'Computational photography extends digital photography by providing the capability to record much more information and by offering the possibility of processing this information afterward'. (Oliver Bimber)

With the rapid development of Machine Learning methods, especially neural networks, more tasks concerning computational photography can be better handled and thus may yield some quite interesting applications, e.g., image completion, HDR image reconstruction, color segmentation and image colorization, or even image style transfer. 

Additionally, it is also possible to infer 3D shape information from 2D images. Learning 3D models from images or even sketches provides a powerful paradigm for geomertic modeling. Features of corresponding 3D models may also be learned with new learning algorithms.

Going beyond Euclidean data, Deep Learning has also been used to deal with tasks concerning geometry or graphs, which is a relatively new yet promising research area.

Prüfung

Die Erfolgskontrolle erfolgt durch Bewertung der Präsentation (70%) und der Ausarbeitung des Vortragsmanuskriptes (30%).

Achtung!

Seats are limited in this seminar, students who are interested in attending this seminar please send a contact email with your name and student number to this email address: chengzhi.wu@kit.edu

First come, first served. ;-)

Themenliste

Below are the relevant topics and possible papers that will be discussed in this seminar.