Overview

The education in the area of digital image processing and computer vision is split into basic courses and advanced courses on specific topics. Basic courses are Mustererkennung, Rechnersehen 1, Rechnersehen 2 and Visuelle Objekterkennung. They provide knowledge to successfully work in industrial research and development later. Furthermore, finishing most of them is highly recommended before applying for project and thesis works at our group.

In addition to these basics course there are advanced courses on different specific topics like signal processing, deep learning, state estimation, or machine learning and data mining. Finally, there are seminars and labs (practical projects) each semester for more research-oriented courses.

Typically, our courses are accompanied by practical programming exercises for a hands-on experience of learned concepts and algorithms. There is a special focus on implementing computer vision algorithms efficiently, and working with real world data examples. Without implementing methods and applying them to own real world images, the essential concepts behind image processing and computer vision techniques are way harder to grasp.

Summer Term 2026

ECTS	Type	Title
3	2V	Einführung in Large Language Models
6	3V+1U	Rechnersehen 2
6	4V	Mustererkennung
3	2V	Spezielle Probleme im Rechnersehen
6	2V+2U	Algorithmische Grundlagen / Grundlagen des Programmierens in Python (Teil 1)
3	1V+1U	Programmieren in C++
3	2V+2U	Objektorientierte Programmierung mit C++ (ASQ)

Winter Term 2025/2026

ECTS	Type	Title
3	2V	Einführung in tiefe Lernverfahren
6	3V+1U	Rechnersehen 1
6	4V	Maschinelles Lernen und Datamining
3	2V	Zustandsschätzung und Aktionsauswahl
3	2V	Uncertainty in Deep Learning
3	2V	Spezielle Probleme im Rechnersehen
9	2V+4P	Anwendungspraktikum 3-D Rechnersehen (Lab)
3	2V	Werkzeuge der Mustererkennung und des Maschinellen Lernens (Lab)
3	2S	Rechnersehen / Fortgeschrittene Methoden im Rechnersehen (Seminar)

Final Theses

If you are interested in doing a Bachelor’s or Master’s thesis in our group, please contact a staff member by mail or simply come to our offices! Topics are always a subject to change and depend on the current ongoing research in our group. In order to get an idea for potential topics, please have a look in our research section. Of course, you are very welcome to contribute your own wishes and ideas for thesis topics.

To get an impression of how topics at our group look like and which areas are of interest at the moment, here is a list of finished theses of the last twelve months (M – Master thesis, B – Bachelor thesis):

2026	B	Juliane Thum	Investigating the Link Between Stable Features Learning and Worst-Subgroup Optimization
2026	B	Matti Tennert	Classification of Cineastic Properties in Movies using Vision Foundation Models
2026	B	Magnus Bauer	Debiasing Models Using Concept Activation Vectors
2026	B	Merle Göner	Visualizing CAV-Based Interventions in the Input Space
2026	B	Lucie Wolf	Learning Causal Relationships between Inlet Flow Disturbances and Fan Noise in Turbines
2025	B	Melina Schmelzer	Development and Implementation of Automated Image Analysis Pipelines for Evaluating
			Robustness Tests and Microscope Performance
2025	M	Eric Günl	Enhancing Machine Learning Model Performance through Synthetic Time-Series Eyetracking Data
2025	M	Mario Baars	Disentangle Neurons of Vision Models with Sparse Autoencoders