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Franziska Schirrmacher, M. Sc.

  • Organization: Department of Computer Science
  • Working group: Chair of Computer Science 1 (IT Security Infrastructures)
  • Phone number: +49 9131 85-67651
  • Fax number: +49 9131 85-69919
  • Email: franziska.schirrmacher@fau.de
  • Website:
  • Address:
    Martensstr. 3
    91058 Erlangen
    Room 12.139

PhD candidate in the multimedia security group and member of the SFB/TRR 89 Invasive Computing Project C5 “Security in Invasive Computing Systems“.

Recent Projects:

Below you will find my recent projects. Within the last 2 years I worked on super-resolution of indecipherable license plates and license plate recognition. In my Master Thesis I developed the Quantile Sparse Image (QuaSI) prior and its adaptive version (AQuaSI) under the supervision of Thomas Köhler.

License Plate Recognition:

Due to the recent success of neural networks for image classification tasks, we investigate the performance of neural networks on images of highly degraded license plates for character recognition.

Super-resolution of indecipherable license plates:

Ideal case Reality

In police investigations, a severely degraded image quality leads to an impairment of their success. License plates might give a hint on the suspect, but are often indecipherable. The degradation of the image quality is mainly caused by compression artifacts, low contrast and a low resolution. In order to improve the image quality such that the license plate can be deciphered, super-resolution algorithms are applied to video frames in a post-processing step. Unfortunately, the registration of the frames is not sufficiently accurate to obtain visually pleasing images. Additionally, compression often leads to a loss of information such that super-resolution is not successful.

(Adaptive) Quantile Sparse Image Prior:

Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the solution space. A key challenge in research on image processing is to find suited priors to represent natural images. We propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.
github

Open topics for Bachelor and Master theses:

In case you are interested in license plate recognition or to learn high dimensional filters from data please contact me via mail. For questions regarding other topics in the field of multimedia security you can also write a mail.

Publications:

2019

2018

2017