88th Heidelberger Image Processing Forum: AI for Image Sensing
On Tuesday, November 19th, 2024 at 10:00 AM, Prof. Dr. Michael Möller and Dr. Nico Piatkowski from the Lamarr Institute will participate as speakers in the 88th Heidelberger Image Processing Forum, themed “AI for Image Sensing” (KI für Bildsensorik). This event, co-hosted by the Center for Sensor Systems (ZESS) at the University of Siegen in collaboration with the Lamarr Institute for Machine Learning and Artificial Intelligence, will focus on the integration of AI methods in image sensing technology.
With the theme “AI for Image Sensing,” the forum will take stock of the current advancements in machine learning for image acquisition and design methods. It will explore the present state of these technologies, assess what is already applicable in industrial settings, and discuss future perspectives for AI in this field. The forum will take place at the University of Siegen, with the lectures held in Building US-S and the Open ZESS Tour, at the ZESS building.
Prof. Dr. Michael Möller is a professor of Computer Vision at the University of Siegen, head of the research group Learning to Sense (L2S) and a Lamarr Fellow. He will present his group’s cutting-edge research on how machine learning can be integrated with hardware for optimizing image sensor systems. Dr. Nico Piatkowski, researcher on Machine Learning for Quantum Computers at Lamarr, will focus on the importance of resource-efficient AI and how these technologies can drive innovation in sensor systems.
A tour of the ZESS facilities will be a key highlight of the forum, offering participants the opportunity to explore the center’s extensive research and engage directly with scientists from ZESS and the local industry. The midday break, accompanied by an exhibition and poster session, will provide additional opportunities for networking and exchanging ideas.
To register, please visit this page.
Details
Date
19. November 2024
10:00 - 17:30
Location
University of Siegen
Topics
Resource-aware Machine Learning , Science