Universität Wien

250007 VU Imaging and Visualization (2020S)

4.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Prüfungsimmanente Lehrveranstaltung
Moodle

Details

Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Home learning: the lectures are given remotely, same day as planned before, at 09:00. Last lecture on Thursday, 26/03.

Please bring your laptop with you (if possible with Python, included numpy and matplotlip, installed).

  • Dienstag 03.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 05.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 10.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 17.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 19.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 24.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 26.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 31.03. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 02.04. 08:00 - 11:15 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

In this course, we will focus on variational methods for standard imaging techniques like denoising, deblurring, inpainting [filling missing parts of an image] or segmentation [dividing an image into different regions of interest]. We will develop a suitable mathematical framework for these problems making use of regularization with total variation.
We will present some basic tools from convex analysis and geometric measure theory to derive some geometrical properties of this regularizer. A primal dual algorithm will be used to compute solutions to these minimization problems.
Finally, we will see how to visualize the result of a 3D segmentation.
The students will be expected to implement, in Python/Matlab, an explicit solution to some of these problems.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Oral Exam

Mindestanforderungen und Beurteilungsmaßstab

The students are expected to understand the content of the course, including the implementation aspects.
The final grade will take into account the solutions to the exercises which are given during the course (30%) + the result of the oral exam (70%).

Prüfungsstoff

Content of the course.

Literatur

-- Vese, Luminita A., and Carole Le Guyader. Variational methods in image processing. CRC Press, 2016.
-- Giovanni Leoni, A First Course in Sobolev Spaces, Graduate studies in mathematics, American Mathematical Soc., 2009.
-- Chambolle, Antonin, et al. "An introduction to total variation for image analysis." Theoretical foundations and numerical methods for sparse recovery 9.263-340 (2010): 227.

Zuordnung im Vorlesungsverzeichnis

MAMV

Letzte Änderung: Mo 07.09.2020 15:21