Computer Vision, Perception and Cognition Group

“AI is THE key technology of the digital transformation, across sectors and industries, with major effects on our societies. Our research thus makes major contributions to the development of robust and trustworthy AI methods, and we enthusiastically teach their safe implementation and application.”

Professor Dr Thilo Stadelmann

Fields of expertise

Pattern recognition with deep learning
Machine perception, computer vision and speaker recognition
Neural system development

The CVPC group conducts pattern recognition research, working on a wide variety of tasks relating to image, audio, and signal data per se. We focus on deep neural network and reinforcement learning methodology, inspired by biological learning. Each task we study has its own learning target (e.g., detection, classification, clustering, segmentation, novelty detection, control) and corresponding use case (e.g., predictive maintenance, speaker recognition for multimedia indexing, document analysis, optical music recognition, computer vision for industrial quality control, automated machine learning, deep reinforcement learning for automated game play or building control), which in turn sheds light on different aspects of the learning process. We use this experience to create increasingly general AI systems built on neural architectures.

Services

Insight: keynotes, trainings
AI consultancy: workshops, expert support, advise, technology assessment
Research and development: small to large-scale collaborative projects, third party-funded research, student projects, commercially applicable prototypes

Projects

Team

Head of Research Group

Prof. Dr. Thilo Stadelmann

+41 (0) 58 934 72 08
thilo.stadelmann@zhaw.ch

Publications

Stadelmann, Thilo; Keuzenkamp, Julian; Grabner, Helmut; Würsch, Christoph,

2021.

The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid.

Education Sciences.

11(7), pp. 318.

Available from: https://doi.org/10.3390/educsci11070318
Simmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter; Rosenthal, Matthias; Stadelmann, Thilo,

2021.

A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [paper].

In:

Proceedings of the 8th SDS.

8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021.

IEEE.

pp. 26-31.

Available from: https://doi.org/10.1109/SDS51136.2021.00012
Knapp, Evelyne; Battaglia, Mattia; Stadelmann, Thilo; Jenatsch, Sandra; Ruhstaller, Beat,

2021.

XGBoost trained on synthetic data to extract material parameters of organic semiconductors [paper].

In:

Proceedings of the 8th SDS.

8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021.

IEEE.

pp. 46-51.

Available from: https://doi.org/10.1109/SDS51136.2021.00015
Iwane, Fumiaki; Iturrate, Iñaki; Chavarriaga, Ricardo; Millán, José del R.,

2021.

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states.

Journal of Neural Engineering.

18(4), pp. 046044.

Available from: https://doi.org/10.1088/1741-2552/abfa70