Performance of a path tracing task using stereo and motion based depth cues

Stereoscopic displays have a number of properties that could be advantageous in the field of medical diagnosis. The aim of the current study is to get a better understanding of the relative importance of motion based depth cues (object motion, movement parallax) and stereoscopic disparity on the performance of a path tracing task, representative of angiographic visualizations. To date, these cues have not frequently been combined in a single study that would allow a direct comparison of their effects. In this paper, we report on an experiment where we measured the effectiveness of motion-based cues and stereoscopic disparity in terms of completion time, number of errors, perceived workload and perceived discomfort. Results revealed that both object motion and movement parallax enhanced performance in terms of number of correct answers. However, object motion was superior to motion parallax on self-report of mental workload and visual comfort. Stereoscopic disparity significantly decreased completion times when combined with object motion or movement parallax. On accuracy, no effect of stereo was found

Interactive Visual Analytics for Large-scale Particle Simulations

Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters. The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization

Visualising multiple overlapping classification hierarchies.

The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they tryto reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies. The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer. This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies. Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation