
Dipl.-Ing. Vincent Lassen
Wissenschaftliche Mitarbeitende
Adresse
Zimmer: 02.031 | Cauerstraße 7, Geschoss: 02
Kontakt
- E-Mail: vincent.lassen@fau.de
Vincent Lassen is a research associate and PhD student at the Chair of Intelligent Technical Electronics and Systems (LITES) at Friedrich-Alexander University Erlangen-Nürnberg (FAU). After completing his mechanical engineering degree at TU Dresden, he worked as a research associate at the Vodafone Chair under Prof. Gerhard Fettweis, focusing on XR technologies (AR, MR, VR) and their integration into demonstrators, as well as producing digital content for various research projects.
He is currently contributing to the development of an XR lab at FAU that brings together technologies from the fields of extended reality, AI, and sensor systems for research, industry, and the public. As part of his doctoral research, he develops AI-based multi-camera systems for robust environmental perception under real-world conditions. His work focuses on combining CNN-based object detection, homography transformations, and cross-sensor data fusion to enable precise scene understanding – with applications in parking surveillance, smart city systems, and XR-based digital twins.
since 09/2022 | Wissenschaftlicher Mitarbeiter und Doktorand Lehrstuhl für Intelligente Technische Elektronik und Systeme Department Elektrotechnik-Elektronik-Informationstechnik (FAU) |
12/2020-08/2022 | Wissenschaftlicher Mitarbeiter Vodafone Chair für Mobile Kommunikations Systeme Technische Universität Dresden (TUD) |
10/2020-12/2020 | Wissenschaftlicher Hilfsarbeiter Lehrstuhl für Agrarsystemtechnik Technische Universität Dresden (TUD) |
10/2014-09/2020 | Dipl.-Ing. Maschinenbau, “Augmented Reality in der Landwirtschaft” Technische Universität Dresden (TUD) Schwerpunkte: Augmented Reality, App Entwicklung, Java, App Design Diplomarbeit: “Use of augmented reality for autonomous vehicles in fruit production” |
Extended Reality Technologies (AR, VR, MR, XR)
Multi-Camera Systems and Sensor Fusion
Computer Vision and Deep Learning for Real-World Environments
Parking Occupancy Detection through Adaptive Multi-Sensor Camera-CNN Fusion
In: IEEE Sensors Letters (2025)
ISSN: 2475-1472
DOI: 10.1109/LSENS.2025.3593908
BibTeX: Download
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Real-Time Instance Segmentation for Parking Occupancy Detection Using CNNs
2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media (Trondheim, Norwegen)
DOI: 10.1109/CISM64958.2025.11060860
BibTeX: Download
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The Demand and the Path to Trustworthy Continuous Blood Glucose Monitoring
In: Current Directions in Biomedical Engineering (2025)
ISSN: 2364-5504
BibTeX: Download
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German Perspective on 6G – Use Cases, Technical Building Blocks and Requirements
Insights by the 6G Platform Germany
Erlangen: FAU University Press, 2024
(FAU Studien aus der Elektrotechnik, Bd.28)
ISBN: 978-3-96147-797-5
DOI: 10.25593/10.25593/978-3-96147-797-5
URL: https://open.fau.de/handle/openfau/33572
BibTeX: Download
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