Prof. Dr. Daniel Cremers



Contact Details

Visitenkarte in TUMonline

Academic Career and Research Areas

Professor Cremers conducts research on mathematical image processing and pattern recognition. The objective of this research is to improve the ability of machines to analyze and interpret image data. His research focuses on convex optimization methods, partial differential equations, graph theory algorithms and statistical inference. Professor Cremers is a co-editor of the International Journal of Computer Vision, IEEE Transactions on Pattern Recognition and Machine Intelligence and the SIAM Journal of Imaging Sciences. 

After studying physics and mathematics at Heidelberg University, Indiana State and Stony Brook, Professor Cremers was awarded a doctorate in computer science in 2002 at the University of Mannheim. Following this, he worked as a postdoctoral researcher at UCLA. In 2004, he joined Siemens Corporate Research (Princeton) as a member of staff. In 2005, he accepted an appointment to a professorship at the University of Bonn. Professor Cremers has been Full Professor of Computer Vision and Artificial Intelligence at TUM since 2009.


Cremers 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.


Pock T, Cremers D, Bischof H, Chambolle A: “Global Solutions of Variational Models with Convex Regularization”. SIAM Journal on Imaging Sciences. 2010; 3(4): 1122-1145. 


Schoenemann 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.


Cremers D: “Dynamical statistical shape priors for level set based tracking”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006; 28(8): 1262-1273.


Cremers 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.