October 24/25, 2023, Meduni Vienna
The Austrian Association for Pattern Recognition (OAGM) invites authors and guests to participate in its 46th annual workshop, held on October 24 and 25, 2023 at Meduni Vienna. The workshop will bring together the interdisciplinary scientific community in the areas of machine learning, artificial intelligence, computer vision, and biomedicine in Austria and nearby countries to present and discuss recent research and the most important directions in this rapidly evolving field, with impact across many disciplines.
This year, we have defined an interdisciplinary impulse theme of one health to engage and build a bridge to the fields of pattern recognition and machine learning.
Further topics of interest include but are not limited to:
Methodological novelty in and applications of
1) Pattern recognition
2) Machine learning
3) Artificial intelligence
4) Computer vision
- Georg Langs (Computational Imaging Research Lab, Meduni Vienna)
- Peter M. Roth (Institute of Computational Medicine, VetMedUni Vienna)
The workshop will be held at the Medical University of Vienna
To encourage the linking across communities active in the subject areas, we solicit
- Full papers (max. 6 pages): novel, original, unpublished work.
- Spotlight abstracts (max. 2 pages including one figure): exciting novel initial results or solutions of practical problems.
Paper/abstract lengths exclude references. All submitted papers and abstracts will undergo a double-blind peer review process by the program committee. We encourage in particular students to submit their work, to engage in an interdisciplinary exchange during the OAGM workshop.
Publication: Accepted papers and spotlight abstracts will be published online with Verlag der TU Graz in a fully open-access electronic volume (with ISBN, and DOIs for individual papers and abstracts).
Submission platform: Please submit papers via
In addition, there will be two awards:
1) OCG Best Paper Award for the best full paper
2) Best Student Paper Award for the best paper submitted by a student