Universität Wien

250007 VU Imaging and Visualization (2020S)

4.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Continuous assessment of course work
Moodle

Details

Language: English

Lecturers

Classes (iCal) - next class is marked with N

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).

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

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

Oral Exam

Minimum requirements and assessment criteria

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%).

Examination topics

Content of the course.

Reading list

-- 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.

Association in the course directory

MAMV

Last modified: Mo 07.09.2020 15:21