L-KONIG.COM - Key Persons
Job Titles:
- Scientific Staff Member
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- Young Scientist Group Leader / German Center for Vertigo and Balance Disorders
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
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- Siemens Corporate Research
Job Titles:
- Scientific Staff Member
- Senior Research Scientist / Chair for Computer Aided Medical Procedures & Augmented Reality
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- Research Assistant
- Scientific Staff Member
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Job Titles:
- Scientific Staff Member
- Personal Information
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Job Titles:
- Construction of Data Flow Networks for Tracking in Augmented Reality Applications
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Job Titles:
- Scientific Staff Member
- Senior Research Scientist
- Senior Research Scientist at the Technical University of Munich
Daniel Roth is a senior research scientist at the Technical University of Munich, Germany. Before joining the Technical University of Munich, he received his doctoral degree in Computer Science in 2019 from the University of Würzburg, Germany. In his thesis, he focused on intrapersonal and interpersonal aspects of virtual reality as well as hybrid avatar-agent simulations. In 2014, he graduated in Media- and Imaging Technology from the University of Applied Sciences in Cologne (TH Köln). His main research interests are virtual- and augmented reality technologies for physical and mental health applications and computer aided interventions.
My focus is on human-centered computing and extended reality, especially in medical applications. Aside from my regular teaching duties, I offer topics for guided research projects, bachelor and master thesis. Please contact me to that regard.
Job Titles:
- Chairman of Computer Aided Medical Procedures & Augmented Reality
Professor Menze conducts research in the field of medical image computing. He develops algorithms that analyze biomedical images using models from computational physiology and biophysics. The emphasis of this work is on applications in clinical neuroimaging and the personalized modeling of tumor growth. He has organized workshops on medical computer vision and on neuroimaging at MICCAI, NIPS and CVPR, served as a member of the program committee of MICCAI and is a member of the editorial board of the Medical Image Analysis journal.
Professor Menze studied physics in Heidelberg (Germany) and Uppsala (Sweden) and obtained a Ph.D. in computer science from Heidelberg University in 2007. He subsequently moved to Boston (USA) where he worked as a postdoctoral researcher at Harvard University, Harvard Medical School and MIT. This was followed by a research position at Inria in Sophia-Antipolis (France) and then by a senior researcher and lecturer position at ETH Zurich (Switzerland). In 2013 he was the first scholar to have been appointed a Rudolf Moessbauer Professor at TUM. He now heads the 'Image-based Biomedical Modeling Group' at the Munich School of Bioengineering.
This project studies alternatives for the representation, segmentation and registration of articulated shapes in the absence of a-priori models. The analysis starts with the 3-D reconstruction of the objects (voxelset or mesh) from video sequences obtained with a multiple-camera setup, and with the representation of the voxelset or the mesh as a graph. Relying on the spectral-graph theory and on the spectral embedding techniques (e.g Laplacian Eigenmaps, LLE), a quasi-invariant representation of the shape is built and used for solving the registration and segmentation problems. The Registration consists on finding a dense match between the nodes of wide-separated poses of the same shape. The problem is solved by first aligning the two spectral representations trough the matching of eigenfunction histograms, and then refined with an EM algorithm formulated following the Bayesian framework for clustering. Finally, the Segmentation relies on a spectral clustering method based upon k-wise relationships.