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.”
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
- DIR3CT Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
- TAILOR
- RealScore - Scanning of Real-World Sheet Music for a Digital Music Stand
- FWA –Visual Food Waste Analysis for Sustainable Kitchens
- Ada – Advanced Algorithms for an Artificial Data Analyst
- QualitAI – Quality control of industrial products via deep learning on images
- Libra: A One-Tool Solution for MLD4 Compliance
- DeepScore – Digital Music Stand with Musical Understanding via Active Sheet Technology
- PANOPTES
Team
Head of Research Group
Publications
-
Simmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter; Rosenthal, Matthias; Stadelmann, Thilo,
2021.
In:
8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021.
IEEE.
Available from: https://doi.org/10.21256/zhaw-22256
-
Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [paper].
In:
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Available from: https://doi.org/10.21256/zhaw-22061
-
Bontempi, Gianluca; Chavarriaga, Ricardo; De Canck, Hans; Girardi, Emanuela; Hoos, Holger; Kilbane-Dawe, Iarla; Ball, Tonio; Nowé, Ann; Sousa, Jose; Bacciu, Davide; Aldinucci, Marco; De Domenico, Manlio; Saffiotti, Alessandro; Maratea, Marco,
2021.
The CLAIRE COVID-19 initiative : approach, experiences and recommendations.
Ethics and Information Technology.
Available from: https://doi.org/10.1007/s10676-020-09567-7
-
Aydarkhanov, Ruslan; Ušćumlić, Marija; Chavarriaga, Ricardo; Gheorghe, Lucian; Millán, José del R,
2021.
Closed-loop EEG study on visual recognition during driving.
Journal of Neural Engineering.
18(2),
pp.026010.
Available from: https://doi.org/10.1088/1741-2552/abdfb2
-
Jao, Ping-Keng; Chavarriaga, Ricardo; Dell'Agnola, Fabio; Arza, Adriana; Atienza, David; Millan, Jose del R.,
2021.
EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks.
IEEE Transactions on Human-Machine Systems.
51(2),
pp.99-108.
Available from: https://doi.org/10.1109/THMS.2020.3038339