Functional connectivity, a flourishing new area of research in human neuroscience, carries a substantial challenge for visualization: while the end points of connectivity are known, the precise path between them is not. Although a large body of work already exists on the visualization of anatomical connectivity, the functional counterpart lacks similar development. To optimize the clarity of whole-brain and complex connectivity patterns in three-dimensional brain space, we develop mean-shift edge bundling, which reveals the multitude of connections as derived from correlations in the brain activity of cortical regions.

We augment schematic maps of transportation systems by superimposing them on street-level maps that are fitted using image warping techniques. While schematic maps are successful in conveying information about lines and connections in a public transportation network, they usually contain little or no detail describing the environment of stations or their embedding in the surrounding area. The annotation of a distorted city map therefore alleviates this deficiency and improves further the usability of schematic transportation maps by merging two different navigational spaces. Our technique for fitting the street map to the schematic map is based on moving least squares in combination with an overlap control technique. We thus obtain an easily readable transportation network map on which we can show all the typical city map features such as rivers, streets, and parks without compromising on the schematization. Furthermore, for the interactive exploration we couple zooming with warping and control over the level of detail in what we call warping zoom.

We employ Laserpointer-Interaction as an intuitive, direct and flexible interaction concept particularly for large, highresolution displays which enforce physical navigation. We therefore demonstrate general applicability and suitability by applying Laserpointer-Interaction to a wide range of domains -- from scientific applications and formal experiments to artistic installations for the broad public.

Abstract We use the complex logarithm as a transformation for the visualization and navigation of highly complex satellite and aerial imagery. The resulting depictions show details and context with greatly different scales in one seamless image while avoiding local distortions. We motivate our approach by showing its relations to the ordinary perspective views and classical map projections. We discuss how to organize and process the huge amount of imagery in realtime using modern graphics hardware with an extended clipmapping technique. Finally, we provide details and experiences concerning the interpretation of and interaction with the resulting representations.

Commonly known detail in context techniques for the two-dimensional Euclidean space enlarge details and shrink their context using mapping functions that introduce geometrical compression. This makes it difficult or even impossible to recognize shapes for large differences in magnification factors. In this paper we propose to use the complex logarithm and the complex root functions to show very small details even in very large contexts. These mappings are conformal, which means they only locally rotate and scale, thus keeping shapes intact and recognizable. They allow showing details that are orders of magnitude smaller than their surroundings in combination with their context in one seamless visualization. We address the utilization of this universal technique for the interaction with complex two-dimensional data considering the exploration of large graphs and other examples.

In synthetic holography, the emphasis has been on numerical simulation methods for the recording and reconstruction processes, but methods to visualize the calculated results are yet underdeveloped. We show how volume slicing and volume rendering using a threshold for translucency can be applied to synthetic holography. Results of the three-dimensional visualization of the reconstruction are presented, thereby illustrating the usefulness of the threshold operator.