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.”
- 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.
- 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
AC3T – AI powered CBCT for improved Combination Cancer Therapy
The project enables a novel, combined, adaptive cancer therapy combining tumor treating field and radiation therapy due to significantly improved static (3D) and time-resolved (4D) low dose Cone Beam Computer Tomography images based on artificial intelligence image reconstruction algorithms. ...
AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support
Monitoring diverse sensor signals of patients in intensive care can be key to detect potentially fatal emergencies. But in order to perform the monitoring automatically, the monitoring system has to know what is currently happening to the patient: if the patient is for example currently being moved by medical staff, ...
Good practices for responsible development of AI-based applications in healthcare
This project will identify proven methods, practices and standards that support responsible research and development of AI systems for health. They will be tested in use cases from medical imaging and neurotechnology, publicly released and published as a guideline of recommended best practices. ...
Pilot study machine learning for injection molding processes
Researchers from the CAI and InES conduct a technical deep dive together to explore the possibilities of capturing process knowledge on injection molding in deep neural networks and transfer the results to novel usage scenarios. The groups of Prof. Stadelmann (Computer Vision, Perception & Cognition, ZHAW CAI) and ...
Accessible Scientific PDFs for All
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Delaux, Alexandre; Saint Aubert, Jean‐Baptiste; Ramanoël, Stephen; Bécu, Marcia; Gehrke, Lukas; Klug, Marius; Chavarriaga, Ricardo; Sahel, José‐Alain; Gramann, Klaus; Arleo, Angelo,
European Journal of Neuroscience.
54(12), pp. 8256-8282.
Available from: https://doi.org/10.1111/ejn.15190
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
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,
Ethics and Information Technology.
23(Suppl 1), pp. S127-S133.
Available from: https://doi.org/10.1007/s10676-020-09567-7
Aydarkhanov, Ruslan; Ušćumlić, Marija; Chavarriaga, Ricardo; Gheorghe, Lucian; Millán, José del R,
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.,
IEEE Transactions on Human-Machine Systems.
51(2), pp. 99-108.
Available from: https://doi.org/10.1109/THMS.2020.3038339