Maximiliane Gruber
Maximiliane Gruber, M. Sc.
Research
I am working within the field of intelligent video analytics and investigate the influence of various image characteristics on machine vision systems. Such image characteristics depend on the employed image acquisition system (e.g. noise, blur, coding), and on environmental coditions (e.g. light, weather, location). The goal of my work is to reduce the “domain gap” between training and test data in these cases. In my investigations, I regard the machine vision system as black box.
Typical machine vision scenarios comprise object detection and tracking for autonomous driving, license plate recognition, and optical character recognition. Application to further domains like medical imaging are possible.
Offered Thesis
Completed and Ongoing Theses
Master’s Theses
- “Emulation of Image Coding Artifacts Employing Adversarial Learning”
Bachelor’s Theses
- “Document Image Quality Assessment for Text Recognition Systems”
- “Emulation of Image Artifacts by Learning in the Frequency Domain”
- “Setup of a demonstrator for depth estimation employing a stereo camera and a LIDAR”
Research Internships and Project Theses
- “Image quality analysis from decoded video frames”
- “Modeling of environmental influences in Blender using Python”
- “Assessment of the Influence of Video Coding on Text Recognition”
- “Development of a Confidence Metric based on the Quality of Coded Video Sequences”
Awards
- : Siemens-Masterpreis (Siemens) – 2020
Teaching
Tutorial (TUT)
Lecture (VORL)
-
Signals and Systems I
In der Lehrveranstaltung werden grundlegende Kenntnisse über Stromkreise mit
Widerstand, Kapazität und Induktivität vorausgesetzt, ebenso Kenntnisse über
komplexe Zeiger und Übertragungseigenschaften einfacher linearer Netzwerke.
Diese können beispielsweise durch die beiden Module "Grundlagen der
Elektrotechnik I" und "Grundlagen der Elektrotechnik II" oder durch die
Kombination der Module "Einführung in die Informations- und
Kommunikationstechnik" und "Elektronik und Schaltungstechnik" erworben
werden. Für Studenten ohne diese Vorlesungen (beispielsweise im Studiengang
Computational Engineering) können die notwendigen Vorkenntnisse auch im
Selbststudium anhand der Kapitel 2 über Physikalische Grundlagen elektrischer
Schaltungen und Kapitel 3 über Passive Netzwerke aus dem Buch von Oehme,
Huemer, Pfaff, "Elektronik und Schaltungstechnik", Hanser Verlag, München
2007 erworben werden.
Publications
2022
Domain Adaptation for Unknown Image Distortions in Instance Segmentation
IEEE International Conference on Image Processing (ICIP) (Bordeaux, 16. October 2022 - 19. October 2022)
DOI: 10.1109/ICIP46576.2022.9897339
URL: https://arxiv.org/abs/2210.02386
BibTeX: Download
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2021
3D Rendering Framework for Data Augmentation in Optical Character Recognition
2021 International Symposium on Signals, Circuits and Systems (ISSCS) (Iasi, Romania (virtual), 15. July 2021 - 16. July 2021)
In: ISSCS 2021 - International Symposium on Signals, Circuits and Systems 2021
DOI: 10.1109/ISSCS52333.2021.9497438
URL: https://arxiv.org/abs/2209.14970
BibTeX: Download
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