Prof. Dr. Torsten Schön

Prof. Dr. Torsten Schön
Room: K201
Subject Area: Computer Vision for Intelligent Mobility Systems
Faculty: Fakultät I
Vita
  • Since 2020 Reserach professor at THI
  • 2014-2020: Audi AG: Senior Data Scientist for Artificial Intelligence
  • 2013-2014: dotplot GmbH and Clueda AG: Data Scientist
  • 2013-2014: FHM Bamberg: Lecturer for descriptive and inductive statistics
  • 2011-2013: SustSol GmbH: PhD student and software engineer
  • 2011-2013: Universität Regensburg: PhD Student in the Machine Learning group
  • 2010-2013: Softgate GmbH: Software Developer
  • 2008-2013: Self employed: Webdesign and -programming
  • 2005-2010 Hochschule Weihenstephan-Triesdorf: Diploma study of Bioinformatics
Publications

Publications since 2019: see bottom of this page

  • Torsten Schön, Martin Stetter, Elmar W. Lang, Physarum Learner: A bio-inspired way of learning structure from data, Expert Systems With Applications, 2014
  • Torsten Schön, Martin Stetter, Elmar W. Lang, A new Physarum Learner for Network Structure Learning from Biomedical Data, Proceedings of the 6th International Conference in Bio-inspired Systems and Signal Processing BIOSIGNALS 2013, February 11-14, Barcelona, Spain
  • Torsten Schön, Martin Stetter, Elmar W. Lang, Structure Learning for Bayesian Networks using the Physarum Solver, Proceedings of the 11th International Conference on Machine Learning and Applications ICMLA 2012, December 12-15, Boca Raton, Florida, USA
  • Torsten Schön , Alexey Tsymbal , Martin Huber, Gene-pair representation and incorporation of GO-based semantic similarity into classification of gene expression data, Intelligent Data Analysis 2012
  • Torsten Schön , Alexey Tsymbal , Martin Huber, Gene-Pair Representation and Incorporation of GO-based Semantic Similarity into Classification of Gene Expression Data, Proceedings of the 7th international conference on Rough sets and current trends in computing, June 28-30, 2010, Warsaw, Poland

Computer Vision for Intelligent Mobility Systems

The Computer Vision for Intelligent Mobility Systems research group is concerned with deep learning methods for analysing and generating image data. Data from different imaging sensors in two- and three-dimensional space are processed. The aim of the research group is to develop super-human perception for automated vehicles, aircraft, rail vehicles and other means of transport, and to analyse image data from infrastructure sensors for traffic monitoring.
In addition to the efforts in the mobility sector, the research group is committed to the use of computer vision for improved environmental protection and more sustainability.

Lectures

Lectures:

Former Lectures:

 

Executive Education:

 

Open Positions

You are interested in computer vision and my research group? Feel free to contact me for currently open positions!

 

Scientific staff

Currently none

 

Bachelor or Master thesis

Your thesis in on one hand the conclusion of your Education, but on the other hand it also the start into your professional career!

Take the chance to use your thesis to dive into one of the most attractive future technology: Artificial Intelligence!

I offer different topics within the field of computer vision, especially in deep learning, with applications for intelligent mobility systems. I can either offer attractive topics from within my research group or get you in touch with interesting contact from the industry.

Open positions at moodle

Interested but no suitable thesis description online? Feel free to drop me an e-mail!

 

Student assistance jobs

Unfortunately, there are no open positions at this time.

Members of the research group

Daniel Kriegl
PhD student

+49 841 9348-2349
Muhammad Saad Nawaz
PhD student (extern)
Dominik Rößle
PhD Student

+49 841 9348-6603
Luca Schreiber


+49 841 9348-2851
Venkatesh Thirugnana Sambandham
PhD Student

+49 841 9348-6535
Sebastian Vauth
Research Associate Digitalization of Teaching AKI

+49 841 9348-2346
Xujun Xie
PhD Student

+49 841 9348-6481

Publications

2024
WACHTEL GRANADO, Diogo, Samuel QUEIROZ, Torsten SCHÖN, Werner HUBER und Lester FARIA, 2024. A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, Page3964-3969. ISBN 979-8-3503-9946-2. Available at: https://doi.org/10.1109/ITSC57777.2023.10422067
GERNER, Jeremias, Dominik RÖSSLE, Daniel CREMERS, Klaus BOGENBERGER, Torsten SCHÖN und Stefanie SCHMIDTNER, 2024. Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation. In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, Page2396-2403. ISBN 979-8-3503-9946-2. Available at: https://doi.org/10.1109/ITSC57777.2023.10422398
2023
SOUZA, Bruno J., Lucas C. DE ASSIS, Dominik RÖSSLE, Roberto Z. FREIRE, Daniel CREMERS, Torsten SCHÖN und Munir GEORGES, 2023. AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication. In: 2022 2nd International Conference on Computers and Automation (CompAuto 2022): Proceedings. Piscataway: IEEE, Page42-47. ISBN 978-1-6654-8194-6. Available at: https://doi.org/10.1109/CompAuto55930.2022.00015
RÖSSLE, Dominik, Lukas PREY, Ludwig RAMGRABER, Anja HANEMANN, Daniel CREMERS, Patrick Ole NOACK und Torsten SCHÖN, 2023. Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset. Plant Phenomics, 5, 68. ISSN 2643-6515. Available at: https://doi.org/10.34133/plantphenomics.0068
DE ANDRADE, Mauren Louise S. C., Matheus VELLOSO NOGUEIRA, Eduardo CANDIOTO FIDELIS, Luiz Henrique AGUIAR CAMPOS, Pietro LO PRESTI CAMPOS, Torsten SCHÖN und Lester DE ABREU FARIA, 2023. Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data. In: RADEVA, Petia, Giovanni Maria FARINELLA und Kadi BOUATOUCH, Editors Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4. Setúbal: SciTePress, Page262-271. ISBN 978-989-758-634-7. Available at: https://doi.org/10.5220/0011672400003417
RADTKE, Henrik, Henrik BEY, Moritz SACKMANN und Torsten SCHÖN, 2023. Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning. In: IEEE IV 2023 IEEE Intelligent Vehicles Symposium: Proceedings. Piscataway: IEEE. ISBN 979-8-3503-4691-6. Available at: https://doi.org/10.1109/IV55152.2023.10186547
2022
SCHÖN, Torsten, 2022. Artificial Intelligence Inspired by Human Learning. In: SCHOBER, Walter, Editors AI. Mobility. Science. Brazil - Germany 2021/22. Ingolstadt: Technische Hochschule Ingolstadt, Page34-37. ISBN 978-3-00-071542-6. Available at: https://www.yumpu.com/en/document/view/67041540/bascinet-ai-mobility-science
RÖSSLE, Dominik, Daniel CREMERS und Torsten SCHÖN, 2022. Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion. In: PIMENIDIS, Elias, Plamen ANGELOV, Chrisina JAYNE, Antonios PAPALEONIDAS und Mehmet AYDIN, Editors Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I. Cham: Springer, Page599-610. ISBN 978-3-031-15918-3. Available at: https://doi.org/10.1007/978-3-031-15919-0_50
2021
WENZEL, Patrick, Torsten SCHÖN, Laura LEAL-TAIXÉ und Daniel CREMERS, 2021. Vision-based mobile robotics obstacle avoidance with deep reinforcement learning. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, Page14360-14366. ISBN 978-1-7281-9077-8. Available at: https://doi.org/10.1109/ICRA48506.2021.9560787
2019
2016
SCHÖN, Torsten, Martin STETTER, O. BELOVA, A. KOCH, Ana Maria TOMÉ und Elmar W. LANG, 2016. Physarum Learner: A Slime Mold Inspired Structural Learning Approach. In: ADAMATZKY, Andrew, Editors Advances in Physarum Machines: Sensing and Computing with Slime Mould. Cham: Springer, Page489-517. ISBN 978-3-319-26662-6. Available at: https://doi.org/10.1007/978-3-319-26662-6_25
2014
SCHÖN, Torsten, Martin STETTER, Ana Maria TOMÉ, Carlos Garcia PUNTONET und Elmar W. LANG, 2014. Physarum Learner: A bio-inspired way of learning structure from data. Expert Systems with Applications, 41(11), 5353-5370. ISSN 0957-4174. Available at: https://doi.org/10.1016/j.eswa.2014.03.002
2013
SCHÖN, Torsten, Martin STETTER, Ana Maria TOMÉ und Elmar W. LANG, 2013. A New Physarum Learner for Network Structure Learning from Biomedical Data. In: ALVAREZ, Sergio A., Jordi SOLÉ-CASALS, Ana FRED und Hugo GAMBOA, Editors Proceedings of the International Conference on Bio-inspired Systems and Signal Processing BIOSTEC. Setúbal: SciTePress, Page151-156. ISBN 978-989-8565-36-5. Available at: https://doi.org/10.5220/0004227401510156
2012
SCHÖN, Torsten, Alexey TSYMBAL und Martin HUBER, 2012. Gene-pair representation and incorporation of GO-based semantic similarity into classification of gene expression data. Intelligent Data Analysis, 16(5), 827-843. ISSN 1088-467X. Available at: https://dl.acm.org/doi/10.5555/2595525.2595531
SCHÖN, Torsten, Martin STETTER und Elmar W. LANG, 2012. Structure Learning for Bayesian Networks Using the Physarum Solver. In: WANI, M. Arif, Taghi KHOSHGOFTAAR, Xingquan ZHU und Naeem SELIYA, Editors Proceedings, 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, Volume 2. Los Alamitos: IEEE, Page488-493. ISBN 978-1-4673-4651-1. Available at: https://doi.org/10.1109/ICMLA.2012.89