4 results
Search Results
Now showing 1 - 4 of 4
Item HyperStreamball Visualization for Symmetric Second Order Tensor Fields(The Eurographics Association, 2006) Liu, J.; Turner, M.; Hewitt, W. T.; Perrin, J. S.; Louise M. Lever and Mary McDerbyThis paper proposes a new 3D tensor glyph called a hyperstreamball that extends streamball visualization used within fluid flow fields to applications within second order tensor fields. The hyperstreamball is a hybrid of the ellipsoid, hyperstreamline and hyperstreamsurface. With the proposed system a user can easily interactively change the visualization. First, we define the distance of the influence function which contributes a potential field that can be designed to highlight the three eigenvectors and eigenvalues of a real symmetric tensor at any sample point. Second, we discuss the choice of source position and how the user can control the parameter mapping between the field data and the implicit function. Finally, we test our results using both synthetic and real data that shows the hyperstreamball's two main advantages: one is that hyperstreamballs blend and split with each other automatically depending on the tensor data, and the other advantage is that the user can achieve both discrete and continuous representation of the data based on a single geometrical description.Item Adaptive Infrastructure for Visual Computing(The Eurographics Association, 2007) Brodlie, K. W.; Brooke, J.; Chen, M.; Chisnall, D.; Hughes, C. J.; John, Nigel W.; Jones, M. W.; Riding, M.; Roard, N.; Turner, M.; Wood, J. D.; Ik Soo Lim and David DuceRecent hardware and software advances have demonstrated that it is now practicable to run large visual computing tasks over heterogeneous hardware with output on multiple types of display devices. As the complexity of the enabling infrastructure increases, then so too do the demands upon the programmer for task integration as well as the demands upon the users of the system. This places importance on system developers to create systems that reduce these demands. Such a goal is an important factor of autonomic computing, aspects of which we have used to influence our work. In this paper we develop a model of adaptive infrastructure for visual systems. We design and implement a simulation engine for visual tasks in order to allow a system to inspect and adapt itself to optimise usage of the underlying infrastructure. We present a formal abstract representation of the visualization pipeline, from which a user interface can be generated automatically, along with concrete pipelines for the visualization. By using this abstract representation it is possible for the system to adapt at run time. We demonstrate the need for, and the technical feasibility of, the system using several example applications.Item A Lemon is not a Monstar: Visualization of Singularities of Symmetric Second Rank Tensor Fields in the Plane(The Eurographics Association, 2008) Liu, J.; Hewitt, W. T.; Lionheart, W. R. B.; Montaldi, J.; Turner, M.; Ik Soo Lim and Wen TangIn the visualization of the topology of second rank symmetric tensor fields in the plane one can extract some key points (degenerate points), and curves (separatrices) that characterize the qualitative behaviour of the whole tensor field. This can provide a global structure of the whole tensor field, and effectively reduce the complexity of the original data. To construct this global structure it is important to classify those degenerate points accurately. However, in existing visualization techniques, a degenerate point is only classified into two types: trisector and wedge types. In this work, we will apply the theory from the analysis of binary differential equations and demonstrate that, topologically, a simple degenerate point should be classified into three types: star (trisector), lemon and monstar. The later two types were mistakenly regarded as a single type in the existing visualization techniques.Item Perlin Noise and 2D Second-Order Tensor Field Visualization(The Eurographics Association, 2005) Liu, J.; Perrin, J.; Turner, M.; Hewitt, W. T.; Louise M. Lever and Mary McDerbyThere has been much research in the use of texture for visulization the vector field data, whereas there has only been a few papers concerned specifically with tensor field data. This set is more complex and embeds more information than vector fields. In this paper, firstly texture is modeled by Perlin Noise. We show that by controlling the parameters of Perlin Noise, the user can control the output texture effectively, which is similar to Spot Noise. Then the modeled texture is used to visualize eigenvector fields of tensor fields by simple convolution. Several examples are shown. Compared to Line Integration Convolution, this method does not need to integrate the streamline along the vector field.