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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 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.