Our research focuses on the development of Computer Vision (CV) and Machine Learning (ML) Systems. Amongst others we are working on the reconstruction of natural habitats, novel imaging techniques, algorithms to quantify the behaviour of animals and the integration of CV and ML into 3D printing and AR & VR applications. Some research projects are listed below.
Image-based tracking of animals in their natural habitats can provide rich behavioural data, but is very challenging due to complex and dynamic background and target appearances. We present an effective method to recover the positions of terrestrial animals in cluttered environments from video sequences.
Despite the success of deep learning-based algorithms their applicability is still limited due to their limited interpretability. In this project we are investigating neuroscientifically inspired explainable AI strategies and advanced neural network architectures such as capsules.
The goal of this project is to develop closed-loop algorithms which make 3D printing as easy as printing a text document. In particular, we want to integrate a variety of sensors like cameras and microphones into these gantry robots and develop new machine learning and computer vision algorithms to monitor the printing process.
Augmented and virtual reality have heavily influenced our ways to perceive and interact with data. In this project we invesetigate the usage of AR and VR techology within a clinical and medical context. In particular we develop novel computer vision, machien learning and computer graphics algorithms end environments for medical education.
Other research projects (partially active, partially completed) can be found below.
We have developed a new FTIR-based Imaging Method (FIM), which is used to detect the contact surface between the arena and the animals to enable high-contrast and high-throughput behavioural analysis. This system in combination with the associated tracking software FIMTrack.
We have implemented Habitat3D, an open source software tool to generate photorealistic meshes from point clouds of natural outdoor scenes. Habitat3D offers a variety of different filtering, clustering, segmentation and meshing routines which can be assembled into pre-defined pipelines operating on either subsets (i.e. clusters) or complete clouds.
Our FTIR-based Imaging Method (FIM) results in an excellent foreground/background contrast so that internal organs and other structures are visible without any complicated imaging or labelling techniques. We demonstrate that FIM enables the precise quantification of locomotion features namely rolling behavior and muscle contractions and we demonstrate that FIM enables automatic in vivo heartbeat quantification of Drosophila melanogaster pupae.
We have developed techniques to segment and track of multiple cells in in vivo wounding. In particular, challenges like overlaps and clustered cell entities are addressed by introducing a statistical measure called hemocyte migration score.
If two cameras are employed to estimate the trajectories of identical appearing objects, calculating stereo and temporal correspondences leads to an NP-hard assignment problem. We study two different types of approaches: probabilistic approaches and global correspondence selection approaches.