The research group “Machine Learning for High-Throughput Methods and Mechatronics” (ML4HOME) deals with the analysis of data being generated automatically by mechatronic systems in big amounts. We aim for models based on labeled / unlabeled images or time-series. Our focus is the generalization of big data-sets consisting of a lot of data points (high-throughput), whose extent requires mandatory automatic processing.

To ensure the usability of all developed algorithms and derived knowledge, we integrate our software typically into the open source MATLAB toolbox SciXMiner.

Team:

• PD. Dr. Markus Reischl
• Moritz Böhland
• Lukas Klinger (DHBW)
• Sophie Kramer (PEBA)
• Friedrich Münke
• Lisa Petani
• Prof. Dr. Christian Pylatiuk
• Jan Schützke
• Mark Schutera (ZF)
• Yanke Wang (CSC)

Program:

BioInterfaces in Technology and Medicine (BIFTM)

Projects:

• Automated Image and Data Analysis
• Bildbasierte Fahrbahnbewertung

Completed Projects:

• TELMYOS
• Establishment of a new method to interactively segment and visualize complex 3D image data