Computer Vision, Perception and Cognition Group
«KI ist DIE Schlüsseltechnologie des digitalen Wandels in allen Branchen und Sektoren und sie hat starke Auswirkungen auf unsere Gesellschaften. Darum leistet unsere Forschung wichtige Beiträge zu robusten und vertrauenswürdigen KI-Methoden, und wir vermitteln mit Begeisterung deren sichere Umsetzung und Anwendung.»
Expertise
- Mustererkennung mit Deep Learning
- Maschinelle Wahrnehmung, Computer Vision und Sprechererkennung
- Entwicklung neuronaler Systeme
Die Computer Vision, Perception and Cognition-Gruppe forscht über Mustererkennung und arbeitet an einer Vielzahl von Aufgaben mit Bild-, Ton- oder allgemein Signaldaten. Wir befassen uns mit der Methodik der tiefen neuronalen Netze (Deep Neural Networks) und des Reinforcement Learning, inspiriert durch biologisches Lernen. Jede unserer Aufgaben hat ihr eigenes Lernziel (z. B. Erkennung, Klassifizierung, Clustering, Segmentierung, Novelty Detection, Steuerung) und ihren eigenen Anwendungsfall (z. B. vorausschauende Instandhaltung (Predictive Maintenance), Sprechererkennung für Multimedia-Indizierung, Dokumentanalyse, optische Notenerkennung, Computer Vision für industrielle Qualitätskontrolle, automatisiertes maschinelles Lernen, Deep Reinforcement Learning für automatisierte Spiele oder Gebäudeleittechnik). Diese werfen ihrerseits ein Licht auf verschiedene Aspekte des Lernprozesses. Wir nutzen diese Erfahrungen, um allgemeinere KI-Systeme zu kreieren, die auf neuronalen Architekturen basieren.
Angebote
- Einblick: Keynotes, Trainings
- KI-Beratung: Workshops, Expertenunterstützung, Beratung, Technikfolgenabschätzung
- Forschung und Entwicklung: kleine bis grosse Gemeinschaftsprojekte, Drittmittelforschung, studentische Projekte, praxiserprobte Prototypen
Team
Head of Research Group
Projekte
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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. ...
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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, ...
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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. ...
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Pilot study machine learning for injection molding processes
Forschende des CAI und InES untersuchen im Rahmen eines technischen Deep Dive gemeinsam die Chancen, Prozesswissen über Spritzgussverfahren in Neuronalen Netzen zu bündeln und auf neue Anwendungsszenarios zu übertragen. Die Gruppen von Prof. Stadelmann (Computer Vision, Perception & Cognition, ZHAW CAI und Prof. ...
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Accessible Scientific PDFs for All
PDF is the most popular document format to provide and distribute information on the internet. It was developed by Adobe 1996 but has been an open format since 2008. It was estimated in 2015 that more than 2.5 trillion PDF documents exist on the internet, covering all aspects of life and research, and their number ...
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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 ...
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Standardized Data and Modeling for AI-based CoVID-19 Diagnosis Support on CT Scans (SDMCT)
Hospitals and research institutes are highly investigating applications of AI in medical imaging. However, developed models and datasets are barely mergeable, and the research results are not reproducible on different datasets due to different CT scanners used. Radiologists told us that “unifying data is crucial for ...
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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.
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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 ...
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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. ...
Publikationen
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Stadelmann, Thilo; Tolkachev, Vasily; Sick, Beate; Stampfli, Jan; Dürr, Oliver,
2019.
Beyond ImageNet : deep learning in industrial practice
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In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 205-232.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_12
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Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt,
2019.
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In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 17-29.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_2
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Stadelmann, Thilo; Stockinger, Kurt; Heinatz-Bürki, Gundula; Braschler, Martin,
2019.
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In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 31-45.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_3
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Stadelmann, Thilo; Braschler, Martin; Stockinger, Kurt,
2019.
Introduction to applied data science
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In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 3-16.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_1
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Stockinger, Kurt; Braschler, Martin; Stadelmann, Thilo,
2019.
Lessons learned from challenging data science case studies
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In:
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 447-465.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_24