Prof. Dr. Daniel Cremers
Academic Career and Research Areas
Prof. Cremers conducts research in the fields of image processing, machine learning and robotics. The aim of this research is to teach machines how to analyze and interpret image data. The methodological focus of his research is on convex optimization, statistical learning and neural networks. Prof. Cremers is/was co-editor of the International Journal of Computer Vision, IEEE Trans. on Pattern Recognition and Machine Intelligence and SIAM Journal of Imaging Sciences and President of the European Computer Vision Association.
After studying physics and mathematics at the Universities of Heidelberg, Indiana State and Stony Brook, Prof. Cremers received his doctorate in computer science from the University of Mannheim in 2002. He then worked as a postdoctoral researcher at UCLA and from 2004 as a member of staff at Siemens Corporate Research (Princeton). In 2005, he accepted an appointment at the University of Bonn. Prof. Cremers has been a full professor of image processing and artificial intelligence at TUM since 2009. Prof. Cremers is Director of the Munich Center for Machine Learning, the Munich Data Science Institute and ELLIS Munich. He is a member of the Bavarian Academy of Sciences and Humanities.
Awards
- European Computer Vision Association Koenderink Test of Time Award (2024)
- Gottfried Wilhelm Leibniz Prize by the DFG (2016)
- European Research Council Starting (2009), Consolidator (2014) and Advanced Grant (2021)
- UCLA Chancellor’s Award for Postdoctoral Research (2005)
- Olympus Prize of the German Association for Pattern Recognition (2004)
Key Publications
Engel J, Schöps T, Cremers D: „LSD-SLAM: Large-Scale Direct Monocular SLAM“. European Conference on Computer Vision. 2014; 834-849.
AbstractCremers D, Kolev K: “Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011; 33(6): 1161-1174.
AbstractSchoenemann T, Cremers D: “A Combinatorial Solution for Model-based Image Segmentation and Real-time Tracking”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010; 32(7): 1153-1164.
AbstractCremers D: “Dynamical statistical shape priors for level set based tracking”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006; 28(8): 1262-1273.
AbstractCremers D, Osher SJ, S. Soatto S: “Kernel density estimation and intrinsic alignment for shape priors in level set segmentation”. International Journal of Computer Vision. 2006; 69(3): 335-351.
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