Universität Wien

250046 SE Archeolab: Computed Tomography and Virtual Unwrapping of Scrolls (2026S)

4.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Continuous assessment of course work
Moodle
We 22.04. 11:30-13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The first class will be held on Wednesday, March 11th. There will be no class on March 4th.

  • Wednesday 04.03. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 11.03. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 18.03. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 25.03. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 15.04. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 29.04. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 06.05. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 13.05. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 20.05. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 27.05. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 03.06. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 10.06. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 17.06. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 24.06. 11:30 - 13:00 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Students will be able to…
- describe the Vesuvius Challenge and related problems.
- explain the Radon transform and filtered back-projection, and reconstruct a 3D image from projection data.
- apply a genetic fit algorithm to optimize script roll segmentation.
- plan experimental setups, capture projection data, and apply the reconstruction pipeline to their own datasets.

Contents: Radon transform, (filtered) backprojection, sampling/noise, artifacts, segmentation, genetic algorithms, deep learning for letter recognition (optional)

Prerequisite: basic knowledge of Python

Methods: Flipped classroom, group work, discussions, experimental laboratory, programming tasks in Python, regular feedback and reflection, oral presentation, written report

Assessment and permitted materials

Continuous assessment through subtasks, discussions during the seminar, oral presentation of experimental results, written report

Students are allowed to use AI-based tools (e.g. ChatGPT, Copilot) to clarify course-related concepts and terminology, suggest alternative formulations of already written text for language polishing, assist in debugging code by explaining error messages or suggesting fixes to existing code. AI may not be used to generate complete solutions for assignments, design the overall structure of code or analysis, or write substantial parts of reports. Any use of AI that influences submitted work must be explicitly documented.

Minimum requirements and assessment criteria

Minimum requirements: regular attendance, submission of 80% of subtasks, independent work on acquired dataset, delivery of oral presentation, submission of final project report

Assessment criteria: mathematical correctness, quality of modeling, clarity of exposition, reproducibility, participation in discussions

Examination topics

All topics covered in the seminar.

Reading list

Foschiatti, S., Kittenberger, A., & Scherzer, O. (2025). Archeolab: Computed tomography and virtual unwrapping of scrolls. Notices of the AMS.

Feeman, T. G. (2015). The mathematics of medical imaging: A beginner’s guide (2nd ed.). Springer.

Kuchment, P. (2014). The Radon transform and medical imaging. Society for Industrial and Applied Mathematics (SIAM).

Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.

Association in the course directory

MAMS; MSE; MEL

Last modified: Th 26.02.2026 15:47