Working Group Algorithms for Computer Vision, Imaging and Data Analysis

Andreas Weinmann

Andreas Weinmann received the Diplom degree in Mathematics and Computer Science (minor) from Technische Universität München, in 2006, and the PhD degree from Technische Universität Graz, in 2010. He worked as a researcher with the Technische Universtät München and with the Helmholtz Center Munich. Since 2015, he is a professor for Mathematics with focus on Image Processing at Hochschule Darmstadt.

His research interests lie in the intersection of Mathematics, Computer Science and Engineering. In particular, he is interested in the development and application of algorithms in Signal and Image Processing/Computer Vision and in Biomedical Imaging. Considered application domains are for instance Magnetic Particle Imaging and Raman Spectroscopy. Further interests are the modeling and analysis of the developed algorithms. The employed methodology includes numerical optimization, variational regularization of inverse problems and machine learning based on Neural Networks.

Website | Andreas Weinmann

Thomas März

Thomas März received his Diplom degree in Applied Mathematics with minors Computer Science and Electrical Engineering in 2005, and his PhD degree in 2010 both from Technische Universität München. He worked as postdoctoral researcher at the Oxford Centre for Collaborative Applied Mathematics from 2010 to 2014. After that, from 2014 to 2018, he was as senior software engineer with Roxar Ltd. (Oxford) and worked on the further development of Roxar's oil reservoir simulator. Since 2018 he is professor of Mathematics at Hochschule Darmstadt with focus on Applied Mathematics in Engineering.

His research interests are in the overlap of the areas of Numerical Analysis, Mathematical Modeling, Computer Science and Engineering. He focuses on the mathematical modeling of technical problems and their computer aided solution through the development of robust and efficient numerical algorithms. Applications include interpolation of missing data in signals and images (image inpainting), denoising on curved surfaces as well as inverse problems in imaging.

Website | Thomas März

Vladyslav Gapyak

Vladyslav Gapyak received both his Bachelor's and Master's degree in Mathematics at the University of Padua in 2019 and in 2021. Since 2022 he pursues his PhD at Hochschule Darmstadt.

In his research he adresses modeling and regularization problems arising in Magnetic Particle Imaging. He considers classical regularization techniques as well as recently developed approaches based on machine learning.

Vladyslav Gapyak

Tamara Dieter

Tamara Dieter received her Bachelor's degree in Mathematics from the University of Applied Sciences Darmstadt in 2018, followed by a Master's degree in Mathematics in 2020, from the same institution. Since 2021, Tamara has been working on a collaborative project between the University and the German Aerospace Center (DLR), with a focus on drone detection for the protection of terrestrial infrastructures.

Her research is centered around visual object detection (with a particular emphasis on drone detection) using deep learning techniques and contextual information. She also focuses on game-based simulations for the generation of synthetic training data and the evaluation of their effectiveness for real-world application (i.e., simulation-reality gap quantification and reduction).

Tamara Dieter

Vanessa Süßle

Vanessa Süßle received a Bachelor's in Computer Science from Goethe University Frankfurt in 2019, and a Master's degree in Data Science from Hochschule Darmstadt in 2022. Since 2022 she is employed at Hochschule Darmstadt working on the automatic analysis of camera trap data.

Her current work focuses on the individual identification of fur-patterened animal species in cooperation with the University of KwaZulu-Natal and the PanAfrican Programme: The Cultured Chimpanzee.

She considers classical feature matching techniques as well as different neural network architectures.

Vanessa Süßle

Tim Selig

Tim Selig received his Bachelor´s degree in Industrial Mathematics in 2020 at the FHWS in Schweinfurt, and a Master´s degree in Applied Mathematics and Physics from Technische Hochschule Nürnberg in 2022.

Since July of 2022 he is working on the regularization of inverse problems with application to biomedical imaging modalities. He focuses primarily on methods based on machine learning.

Tim Selig

Marcel Stark

Marcel Stark received both his bachelor’s and master’s degree in mechanical engineering at the Darmstadt Universitity of Applied Scienes in 2019 and 2022. He has been employed at Darmstadt University of Applied Sciences since 2023.

His current work deals with the analysis of Raman spectroscopic data with respect to the quantitative determination of the process temperature and the molar fraction of the detected gas species. He considers both classical and modern machine learning methods.

Marcel Stark

Vladyslav Polushko

Vladyslav Polushko completed both his Bachelor's and Master's degree in Optical Technology and Image Processing at the the University of Applied Technology Darmstadt in 2018 and 2021.

Currently, he works with Deep Learning methods for Analysis of Remote Sensing images. The research is being conducted in collaboration with Fraunhofer ITWM in Kaiserslautern.

Vladyslav Polushko


We all acquire data to get information, e.g., we take pictures with our mobile phone's camera. Physicians use imaging devices to infer on person's health conditions, scientists use microscopic data to learn about small organisms, and engineers use imaging based on spectroscopic methods to do diagnostics for chemical reactions.

What is common to all these situations is that the data measured does not directly give the information one is interested in; the measured data needs to be processed and the required information needs to be extracted.

The central topic of ACIDA is the development and application of algorithmic solutions to such kind of problems in Computer Vision, Imaging and related Data Analysis problems.

Considered research topics are as follows:

  • Variational methods of Bayesian MAP estimation type for segmentation, denoising, regularization and reconstruction
  • PDE and/or variational methods for data completion/inpainting problems; PDE-models on curved surfaces
  • Reconstruction techniques for the emerging imaging modality Magnetic Particle Imaging (MPI)
  • Analysis of Raman spectroscopic data using taylored classical and machine-learning based approaches
  • Algorithms for nonlinear data such as phase angles, directions, poses, and positive matrices (nerve fibre orientations) based on differential geometry;
  • Developmemt of algorithmic solutions for Computer Vision problems based on deep learning frameworks (e.g., involving specific segmentation/object tasks).

From a methodological side, we use methods of scientific computing, in particular of numerical optimization. We develop and employ iterative schemes, dynamic programming as well as derivative based methods.