Research Projects

Most recent research projects

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.

  • © CVMLS

    Explainable Deep Learning

    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. 

    More information can be found here...

  • © CVMLS

    Augmented & Virtual Reality for Medical Education

    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.

    More information can be found here...

Other Projects

Previous projects

Other research projects (partially active, partially completed) can be found below.

  • © CVMLS

     Reconstruction of Natural Environments

    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.

    More information can be found here...

  • © CVMLS

     FIM Imaging to Visualise and Quantify Internal Organs

    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.

    More information can be found here...

  • © CVMLS

    Visual 3D Tracking of Multiple Objects

    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. 

    More information can be found here...