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
Synthetic data generation of CoVID-19 CT/X-rays images for enabling fast triage of healthy vs. unhealthy patients
The automatic analysis of X-ray/CT images through artificial intelligence models can be useful to automate the clinical scanning procedure. Nonetheless, the limited access to real COVID patient data leads to the need of synthesizing image samples. The goal of this project is to use existing CT/X-ray image datasets ...
DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes
Project DIR3CT aims at improving the image quality of CBCT images by deep learning (DL) the 3D reconstruction from X-ray images end-to-end. This enables a novel CBCT product to be used during radiation therapy and will allow the use of these images for adaptive treatment.
TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization
The main ambition of TAILOR is to build the capacity of providing the scientific foundations for Trustworthy AI in Europe by developing a network of research excellence centers with a technical focus on combining research excellence in the areas of learning, optimisation and reasoning . The current scientific ...
RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand
ScorePad’s sheet music scanning service works for high quality input; to scale up business, it should work as well for smartphone pictures, used sheets etc. Project RealScore enhances the successful predecessor project by making deep learning adapt to unseen data through unsupervised learning. ...
FWA: Visual Food Waste Analysis for Sustainable Kitchens
A novel approach for a fully automated food waste management solution for commercial kitchens is investigated. Food waste is automatically detected using a new camera device, preprocessed in real-time and classified using machine learning algorithms.
Jao, Ping-Keng; Chavarriaga, Ricardo; Millan, Jose del R.,
IEEE Transactions on Affective Computing.
Available from: https://doi.org/10.1109/TAFFC.2021.3059688
Iwane, Fumiaki; Iturrate, Iñaki; Chavarriaga, Ricardo; Millán, José del R.,
Journal of Neural Engineering.
18(4), pp. 046044.
Available from: https://doi.org/10.1088/1741-2552/abfa70
Chavarriaga, Ricardo; Carey, Carole; Contreras-Vidal, Jose Luis; McKinney, Zach; Bianchi, Luigi,
IEEE Open Journal of Engineering in Medicine and Biology.
2, pp. 71-73.
Available from: https://doi.org/10.1109/OJEMB.2021.3061328
Orset, Bastien; Lee, Kyuhwa; Chavarriaga, Ricardo; Millán, José del. R,
Available from: https://doi.org/10.1101/2021.06.15.448360
Stadelmann, Thilo; Würsch, Christoph,
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-20885