While their importance is increasingly recognized, there remain many challenges in the development of uncertainty visualizations.
We introduce two uncertainty visualizations for 2D bidirectional vector fields: one based on a static glyph
and the other based on animated flow. These visualizations were designed for the task of understanding and interpreting
anisotropic rock property models in the domain of seismic data processing. Aspects of the implementations are discussed
relating to design, interaction, and tasks.
Although a number of theories and principles have been developed to guide the creation of visualizations, it is not always apparent how to apply the knowledge in these principles. We describe the application of perceptual and cognitive theories for the analysis of uncertainty visualizations. General principles from Bertin, Tufte, and Ware are outlined and then applied to the analysis of eight different uncertainty visualizations. The theories provided a useful framework for analysis of the methods, and provided insights into the strengths and weaknesses of various aspects of the visualizations.
Visual simulation can be efficiently performed using programmable graphics hardware. However, in utilizing hardware to maximize throughput, it is important not to constrain interactivity. We present a method of using the graphics hardware while maintaining full interactivity during simulation exploration. This interactivity involves: temporal exploration, data probing and modification, simulation model modification, and user defined visual metadata. Results are shown using our application for exploring a reaction-diffusion simulation.
This paper describes current iterative surface matching methods for registration, and our new extensions. Surface matching methods use two segmented surfaces as features (one dynamic and one static) and iteratively search parameter space for an optimal correlation. To compare the surfaces we use an anisotropic Euclidean chamfer distance transform, based on the static surface. This type of DT was analyzed to quantify the errors associated with it. Hierarchical levels are attained by sampling the dynamic surface at various rates. In using the reduced amount of data provided by the surface segmentation each hierarchical level is formed quickly and easily and only a single distance transform is needed, thus increasing efficiency. Our registrations were performed in a data-flow environment created for multipurpose image processing. The new modifications were tested on a large number of simulations, over a wide range of rigid body transformations and distortions. Multimodality, and multipatient registration tests were also completed. A thorough examination of these modifications in conjunction with various minimization methods was then performed. Our new approaches provide accuracy and robustness, while requiring less time and effort than conventional methods.