Universität Wien
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300121 UE Image analysis and reconstruction of morphological datasets (2020W)

3.00 ECTS (3.00 SWS), SPL 30 - Biologie
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

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

Details

max. 9 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

UPDATE: 1 Woche geblockt Ende Jänner/Anfang Februar, ganztägig. Details folgen

  • Montag 25.01. 09:00 - 16:00 Konferenzraum
  • Dienstag 26.01. 09:00 - 16:00 Konferenzraum
  • Mittwoch 27.01. 09:00 - 16:00 Konferenzraum
  • Donnerstag 28.01. 09:00 - 13:00 Konferenzraum
  • Donnerstag 28.01. 14:00 - 16:00 Konferenzraum
  • Freitag 29.01. 09:00 - 16:00 Konferenzraum

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The course will address image theory, especially what are images and how images can be changed or modified. The acquired skills will particularly focus on image processing and visualization methods of morphological datasets. Open source software such as FIJI (Image J) or drishti will be used during the course. The course does not require any previous knowledge on image processing, but a general interest in the field is expected of the students.
Aim of the course is to convey foundations and methods of image theory, image processing and visualization of morphological datasets. After the course, students should independently be able to use image filters, and also conduct quantitative and qualitative analyses of morphological datasets.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Active participation, analysis and understanding of the sample datasets and demonstrations during the course represent one of the main evaluation criteria. Students will receive short projects in groups that will require the acquired skills for analysis (filtering of datasets, volume and surface calculations, segmentation and visualization of datasets). Own projects of e.g. master or phD students are possible. Die achieved result of the short projects will be evaluated in a short oral examination (methods, results, etc.)

Mindestanforderungen und Beurteilungsmaßstab

Presence during course hours is mandatory. Active participation during the trials and experimenting with the provided datasets account for 50% of the grade. A final presentation on the last day plus discussion on the methods applied accounts for the other 50%

Prüfungsstoff

Comprehension of the various programs and image processing and analysis tools. Presentation at the end of the course.

Literatur

Specific references will be provided during the course. Since we only use open source software, various forum of image analysis websites provide the best help.

Zuordnung im Vorlesungsverzeichnis

MBO 7, MZO W-1

Letzte Änderung: Sa 22.10.2022 00:29