Professors and lecturers

News

• Feature der Region Mainfranken über DIBCO
• Die Fachzeitschrift "Logistik heute" berichtet hier über das Forschungsprojekt DIBCO.
  
• Das Forschungsprojekt "DIBCO - Digitales Behältermanagement mit der Anwendung von Computer Vision" wird ab 1.1.2022 für drei Jahre gefördert (zusammen mit Prof. Dobhan von FWI und Industriepartnern). Für Studierende besteht die Möglichkeit, mit Praktika und Abschlussarbeiten im Bereich Computer Vision am Projekt teilzunehmen. Voraussetzung sind gute Kenntnisse in Mathematik und Programmierung. Bei Interesse bitte einfach kurze Email schreiben.

Teaching

WT 2023/24

• Mathematisches Seminar
• Mathematische Methoden des maschinellen Lernens

ST 2023

• Mathematical Foundations of AI
• Approximationsmethoden 
• Informatik 2

Lab

Lab for Mathematical Methods in Computer Vision and Machine Learning

Bachelor- und Masterarbeiten

Betreuung von Bachelor- und Masterarbeiten in den Bereichen:

• Computer Vision
• Maschinelles Lernen
• Digitale Signal- und Bildverarbeitung 
• Data Science

Bei Interesse bitte per Email Kontakt aufnehmen.

Recent Publications

• M. Storath, A. Weinmann. Smoothing splines for discontinuous signals. Journal of Computational and Graphical Statistics. to appear
• C. Ziegler, J. Ising, A. Dobhan, M. Storath. Computer Vision in Reusable Container Management – Requirements, Conception, and Data Acquisition, Mobility in a Globalised World 2022
• M. Storath, A. Weinmann. Variational regularization of inverse problems for manifold-valued data. Information and Inference: A Journal of the IMA, 2021
• L. Kiefer, S. Petra, M. Storath, A. Weinmann. Multi-channel Potts-based reconstruction for multi-spectral computed tomography. Inverse Problems, 2021
• L. Kiefer, M. Storath, A. Weinmann. An algorithm for second order Mumford-Shah models based on a Taylor jet formulation. SIAM Journal on Imaging Sciences, 2020
• L. Kiefer, M. Storath, A. Weinmann. PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model. Image Processing Online, 2020
• L. Kiefer, M. Storath, A. Weinmann. Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case. Foundations of Computational Mathematics, 2020
• M. Storath, A. Weinmann. Wavelet sparse regularization for manifold-valued data. Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, 18(2):674–706, 2020
  
• 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

• CSSD - Cubic smoothing splines for discontinuous signals
• 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
  
• MumfordShah2D - Algorithms for edge preserving smoothing based on the Mumford-Shah model
• PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model
  

Short CV

• Since 2023: Research Professor for Computer Vision and Machine Learning, Technische Hochschule Würzburg-Schweinfurt
• Since 2018: Professor of Mathematics, Technische Hochschule Würzburg-Schweinfurt
  
• 2016 - 2018: Researcher at the Image Analysis and Learning Group, Universität Heidelberg
• 2013 - 2016: Researcher at the Biomedical Imaging Group, EPFL in Lausanne
• 2013: PhD in Applied Mathematics, TU München
• 2010 - 2013: Researcher at Helmholtz Zentrum München GmbH
  
• 2008 - 2010: Scientific assistant, TU München
• 2009 Honors degree in Technology Management, TU München and LMU München
• 2008 Diplom in Mathematics with Computer Science, TU München

Links

Brücken schlagen: Von der Schulmathematik zur Hochschulmathematik