COMPUTER VISION
In the Computer Vision research area, we develop and apply methods for computational processing and analysis of digital microscopy images and images in the biomedical field, especially for solving problems in biology and medicine. A wide range of biomedical research projects rely on image data which require specifically designed image analysis workflows to support the identification of complex biological processes. In close collaboration with our partners from research and industry, we develop algorithms and software that analyse microscopy images, for example fluorescence microscopy images of cells or other kinds of organic material. Additionally, our computer vision methods are also applied in related fields such as livestock farming and animal tracking or sport analysis. Our focus in algorithm development is mainly on methods of signal processing, machine learning, and computer vision for information extraction and interpretation. Besides filtering and denoising for pre-processing and feature extraction methods, we use machine learning approaches such as deep learning for object detection, segmentation, object tracking and feature extraction for subsequent analyses. In addition to workflows and algorithms, we implement image analysis software (e.g., Spotty) for biological research to automatically detect, track, and analyse cells and particles to support fundamental research and drug development but also general 3D model visualization and augmented reality (AR). Often, we implement our solutions based on tools and frameworks such as MATLAB, OpenCV, and TensorFlow.
