Professoren und Lehrkräfte
Prof. Dr. Martin Storath
Besucheranschrift
Ignaz-Schön-Straße 11
97421 Schweinfurt
97421 Schweinfurt
Raum
1.E.43
Sprechstunden
Vorlesungszeit: Mi. 13:00 - 14:00 oder nach Vereinbarung
Vorlesungsfreie Zeit: Nach Vereinbarung
Vorlesungsfreie Zeit: Nach Vereinbarung
Funktion(en)
Leiter Labor für mathematische Methoden für Computer Vision u. maschinelles Lernen
Lehrgebiete
Teaching
Mathematics
Lab
Lab for Mathematical Methods in Computer Vision and Machine Learning
Publikationen
Publications
Recent Publications
- L. Kiefer, M. Storath, A. Weinmann. An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting. IEEE Transactions on Image Processing, 29:921–933, 2019
- M. Storath, L. Kiefer, A. Weinmann. Smoothing for signals with discontinuities using higher order Mumford-Shah models. Numerische Mathematik, 143(2):423-460, 2019
- M. Esposito, C. Hennersperger, R. Göbl, L. Demaret, M. Storath, N. Navab, M. Baust, A. Weinmann. Total variation regularization of pose signals with an application to 3D freehand ultrasound. IEEE Transactions on Medical Imaging, 38(10):2245–2258, 2019
- A. Bendinger, C. Debus, C. Glowa, C. Karger, J. Peter, M. Storath. Bolus arrival time estimation in dynamic contrast-enhanced MRI of small animals based on spline models. Physics in Medicine and Biology, 64(4):045003, 2019
- D. Fortun, M. Storath, D. Rickert, A. Weinmann, M. Unser. Fast piecewise-affine motion estimation without segmentation. IEEE Transactions on Image Processing, 27(11):5612–5624, 2018
- L. Kiefer, S. Petra, M. Storath, A. Weinmann. Direct MRI segmentation from k-space data by iterative Potts minimization. International Conference on Scale Space and Variational Methods (SSVM), 2019
- M. Weiler, F. Hamprecht, M. Storath. Learning steerable filters for rotation equivariant CNNs. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 849–858, 2018
- K. Bredies, M. Holler, M. Storath, A. Weinmann. Total generalized variation for manifold-valued data. SIAM Journal on Imaging Sciences, 11(3):1785–1848, 2018
- W. Erb, A. Weinmann, M. Ahlborg, C. Brandt, G. Bringout, T. Buzug, J. Frikel, C. Kaethner, T. Knopp, T. März, M. Möddel, M. Storath, A. Weber. Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems, 34(5):055012, 2018
- M. Kiechle, M. Storath, A. Weinmann, M. Kleinsteuber. Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing, 27(4):1994–2007, 2018
- M. Storath, A. Weinmann. Fast median filtering for phase or orientation data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):639–652, 2018
Full list here: Google Scholar
Open Source Software
- Pottslab - Multilabel image segmentation (color/gray/multichannel) based on the Potts model (aka piecewise constant Mumford-Shah model)
- DCEBE - Estimation of bolus arrival times for DCE-MRI signals
- CircleMedianFilter - Fast median filter for circle-valued data, for example signals or images describing phase or orientation
- L1TV - Denoising/reconstruction of piecewise constant signals using the L1TV model