Universität Wien
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

260068 VU Scientific image processing (2021W)

5.00 ECTS (3.00 SWS), SPL 26 - Physik
Prüfungsimmanente Lehrveranstaltung
VOR-ORT
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 15 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Lectures are currently planned on-site but they will be performed online in case restrictions would require it.

  • Donnerstag 07.10. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 14.10. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 21.10. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 28.10. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 04.11. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 11.11. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 18.11. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 25.11. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 02.12. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 09.12. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 16.12. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 13.01. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02
  • Donnerstag 20.01. 09:45 - 12:15 PC-Seminarraum 3, Kolingasse 14-16, OG02

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

AIMS
Most of the advanced methods in modern science are based on acquiring two-dimensional images or movies of a sample/phenomenon of scientific interest. In this course, you will learn the basics of quantitative image processing and analysis for scientific purposes to be able to autonomously design, implement and optimise simple image analysis workflows. You will learn how to squeeze out information out of your images of interest, without altering such information.

CONTENTS
Basics of imaging. Digital images. Bit depth. Color. Lookup tables. Regions of Interest (ROI). Quantitative intensity analysis and algebraic operations with images. Image correlations, convolutions and deconvolutions. Fast Fourier transform and other image transforms. Image filtering (linear, non-linear and Fourier). Image thresholding and segmentation. Image annotation and labelling.

METHODS
The course will make use of interactive lectures based on students participation, and of continuous assessment in the form of individual or group activities to be performed in class or at home. The course will be designed around the free image analysis software ImageJ/Fiji, which the participants are ideally expected to install on their own computer that they will bring to class. However, participants will be also encouraged to implement the workflows in other environments or languages (e.g. Matlab, Labview, C++, Python, Julia, R...) if they want to do so.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Assessment will be performed by means of a combination of weekly in-class and/or homework assignments (50%), plus a final exam (50%).

Mindestanforderungen und Beurteilungsmaßstab

After the assignments and the final exam, a maximum of 100 points will be reachable. 50 points or more are needed for a passing grade, determined as follows:
≥ 50 points: 4
≥ 60 points: 3
≥ 75 points: 2
≥ 90 points: 1

Prüfungsstoff

Content of the lecture

Literatur

Zuordnung im Vorlesungsverzeichnis

M-VAF A 2, M-VAF B

Letzte Änderung: Do 07.10.2021 21:09