headed by Prof. Dr. Jos B.T.M. Roerdink
In information visualization, we concentrate on the visualization of high-dimensional abstract data.
In the biomedical domain, methodologies such as multichannel EEG and diffusion tensor imaging (DTI) are used to extract functional brain networks and pathways. DNA microarray measurements allow us to visualize regulatory gene networks. We employ insights from perception science to improve current visualization techniques.
In astronomy, modern surveys provide not only image data but also catalogues of millions of objects, each object with hundreds of associated parameters. To explore these data sets effectively, new and scalable tools are developed that can cope with the sheer data volume which has entered the tens of terabytes regime. In this research the focus is on feature extraction and interactive visualization techniques for high-dimensional parameter spaces.