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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."

Dr. Frank-Peter Schilling

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

Team

Projects

Publications

See also: https://fpschill.github.io/publications/

  1. J. Montoya, M. Amirian, F.-P. Schilling, T. Stadelmann, R. M. Füchslin, I. Herzig, P. Eggenberger, L. Lichtensteiger, M. Morf, P. Paysan, I. Peterlik, and S. Scheib, “Mitigation of motion-induced artefacts in Cone Beam Computed Tomography using Deep Convolutional Neural Networks,” to be subm. to Med. Phys., 2022.
  2. F.-P. Schilling, D. Flumini, R. M. Füchslin, E. Gavagnin, A. Geller, S. Quarteroni, and T. Stadelmann, “Foundations of Data Science: A Comprehensive Overview Formed at the 1st International Symposium on the Science of Data Science,” Archives of Data Science, Series A, vol. 8, no. 2, pp. 1–20, 2022, doi: 10.21256/zhaw-24982. DOI:10.21256/zhaw-24982
  3. I. Herzig, P. Paysan, S. Scheib, F.-P. Schilling, J. Montoya, M. Amirian, T. Stadelmann, P. Eggenberger, R. M. Füchslin, and L. Lichtensteiger, “Deep Learning-Based Simultaneous Multi-Phase Deformable Image Registration of Sparse 4D-CBCT,” in Proceedings of the American Association of Physics in Medicine Annual Meeting (AAPM 2022), 2022. doi.org/10.21256/zhaw-25181
  4. T. Stadelmann and F.-P. Schilling, Eds., Advances in Deep Neural Networks for Visual Pattern Recognition. MDPI, 2022 [Online]. Available at: www.mdpi.com/journal/jimaging/special_issues/deep_neural_network
  5. N. Simmler, P. Sager, P. Andermatt, R. Chavarriaga, F.-P. Schilling, M. Rosenthal, and T. Stadelmann, “A Survey of Un-, Weakly-, and Semi-Supervised Learning Methods for Noisy, Missing and Partial Labels in Industrial Vision Applications,” in 8th Swiss Conference on Data Science (SDS), 2021, pp. 26–31, doi: 10.1109/SDS51136.2021.00012. DOI:10.1109/SDS51136.2021.00012
  6. F.-P. Schilling and T. Stadelmann, Eds., Artificial Neural Networks in Pattern Recognition. MDPI, 2020 [Online]. Available at: www.mdpi.com/journal/computers/special_issues/ANNPR2020
  7. L. Tuggener, M. Amirian, F. Benites, P. von Däniken, P. Gupta, F.-P. Schilling, and T. Stadelmann, “Design Patterns for Resource-Constrained Automated Deep-Learning Methods,” AI, vol. 1, no. 4, pp. 510–538, 2020, doi: 10.3390/ai1040031. DOI:10.3390/ai1040031
  8. F.-P. Schilling and T. Stadelmann, Eds., Artificial neural networks in pattern recognition : Proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2-4, 2020, vol. Lecture Notes in Computer Science, no. 12294. Springer, 2020. DOI:10.1007/978-3-030-58309-5
  9. M. Amirian, L. Tuggener, R. Chavarriaga, Y. P. Satyawan, F.-P. Schilling, F. Schwenker, and T. Stadelmann, “Two to trust: AutoML for safe modelling and interpretable deep learning for robustness,” Proc. of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020, 2020, doi: 10.21256/zhaw-22061. DOI:10.21256/zhaw-22061
  10. M. Amirian, K. Rombach, L. Tuggener, F.-P. Schilling, and T. Stadelmann, “Efficient deep CNNs for cross-modal automated computer vision under time and space constraints,” Proc. of ECML-PKDD 2019, Würzburg, 2019, doi: 10.21256/zhaw-18357. DOI:10.21256/zhaw-18357
  11. F.-P. Schilling and T. Stadelmann, “Deep Learning in medizinischer Diagnostik und Qualitätskontrolle,” Netzwoche, Special Issue: IT for Health, 2019, doi: 10.21256/zhaw-20163. DOI:10.21256/zhaw-20163