Intelligent Vision Systems Group
"We aim at advancing the state of the art in AI, Deep Learning and Machine Learning research, while at the same time developing tailored solutions to real-world challenging problems which help to advance technology and benefit humanity."
Fields of expertise
- Computer Vision
- Machine Learning Systems (MLOps)
- Trustworthy and certifiable AI
We conduct research primarily in the domain of computer vision using 2-,3- or 4-D image or video data as input and performing classification, object detection or other visual tasks, for which we develop state of the art deep neural network architectures. We are particularly interested in recent developments including vision transformers and gauge equivariant neural networks. Domains of applications include, but are not limited to, industrial quality control, medical imaging and diagnosis (computed tomography), as well as earth (satellites) and sky (radio-astronomy) observation data. We are also interested in hybrid approaches to AI as well as geometric deep learning. Our second main area of interest concerns MLOps, which describes best practices for building complete, production-ready and scalable Machine Learning systems. Finally, we are interested in methods to create safe, trustworthy and certifiable AI systems, which comply with current and future legislation.
Services
- Insight: keynotes, trainings
- AI consultancy: workshops, expert support, advise, technology assessment
- Research and development: small to large-scale research projects, third party-funded research, student projects, commercially applicable prototypes
Team
Head of Research Group
Projects
Unfortunately, no list of projects can be displayed here at the moment. Until the list is available again, the project search on the ZHAW homepage can be used.
Publications
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Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,
2020.
Design patterns for resource-constrained automated deep-learning methods.
AI.
1(4), pp. 510-538.
Available from: https://doi.org/10.3390/ai1040031
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Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
2020.
Artificial neural networks in pattern recognition.
Basel:
MDPI.
Computers ; 9.
Available from: https://www.mdpi.com/journal/computers/special_issues/ANNPR2020
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Schilling, Frank-Peter; Stadelmann, Thilo, eds.,
2020.
9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.
Springer.
Lecture Notes in Computer Science ; 12294.
ISBN 978-3-030-58308-8.
Available from: https://doi.org/10.1007/978-3-030-58309-5
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Stadelmann, Thilo; Schilling, Frank-Peter,
2019.
Deep Learning in medizinischer Diagnostik und Qualitätskontrolle.
Netzwoche.
Available from: https://doi.org/10.21256/zhaw-20163
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Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo,
2019.
Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [paper].
In:
ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-18357