250007 VU Imaging and Visualization (2020S)
Prüfungsimmanente Lehrveranstaltung
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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
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%).
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.
-- 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
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.