Lehrstuhl für Informatik 1 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Franziska Schirrmacher has developed innovative AI-based image processing methods during her academic career and applied them in several socially relevant areas. Her master thesis in the field of medical image processing significantly improves the image quality of eye scans, enabling better diagnosis of eye diseases. The work was presented at the high-level medical image processing conference MICCAI 2017 and published in a special issue of the journal Medical Image Analysis, where it was recognized as one of the best papers. She is currently writing her doctoral thesis at the IT Security Infrastructures Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg. She is currently contributing to a DFG-funded collaborative research center and developing image processing methods to fight organized crime in cooperation with the BKA. One goal, for example, is improved recognition of license plates from poor quality image or video data such as surveillance cameras. The high reliability is achieved by a novel combination of character recognition and image processing. Her published results led to a whole series of follow-up work and serve as the basis for license plate recognition at the BKA as part of a BMBF project.
Questions with regard to Artificial Intelligence
In police investigations, data from various sources are used with the aim of identifying the suspect. One possibility is to determine the license plate number of the crime vehicle, which can be seen on a surveillance video. Often, the quality of the video is poor and the license plate cannot be identified. The resolution of the camera, video compression as well as lighting are known influencing factors. To train a neural network for license plate recognition, these factors must be covered in the training data. If the test data deviates from the training data, the recognition accuracy of the network decreases strongly. The goal of my work is to design a network topology and create a dataset for reliably predicting license plates from poor quality image or video data. To improve the topology, especially the extension of the network with tasks from the field of image processing shows great potential.