Uncertain Flow Visualization using LIC

In this paper we look at the Line Integral Convolution method for flow visualization and ways in which this can be applied to the visualization of two dimensional, steady flow fields in the presence of uncertainty. To achieve this, we start by studying the method and reviewing the history of modifications other authors have made to it in order to improve its efficiency or capabilities, and using these as a base for the visualization of uncertain flow fields. Finally, we apply our methodology to a case study from the field of oceanography

The State of the Art in Flow Visualization: Dense and Texture-Based Techniques

Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods. Categories and Subject Descriptors (according to ACM CCS): I.3 [Computer Graphics]: visualization, flow visualization, computational flow visualizatio

The perceptual optimization of two-dimensional flow visualizations using human-in-the-loop local hill climbing

Flow visualization is the graphical representation of vector fields or fluids that enables an observer to visually perceive the forces or motions involved. The fields being displayed are typically dynamic and complex, with a vector direction and magnitude at every point in the field, and often with additional underlying data that is also of interest to the observer. Distilling this mass of data into a static, two-dimensional image that captures the essential patterns and features in a way that is intuitively understandable can be a daunting task. Historically, there have been many different techniques and algorithms to generate visualizations of a flow field. These methods differ widely in implementation, but conceptually they involve the association of significant aspects of the data field (e.g., direction, velocity, temperature, vorticity) to certain visual parameters used in the graphic representation (e.g., size and orientation of lines or arrows, foreground and background color, density/sparsity of graphical elements). For example, the velocity of a field could be mapped to color, line width, line length, arrow head or glyph size, etc. There are many such potential parameter mappings within each technique, and many value ranges that can be used to constrain each parameter within a given mapping, resulting in a virtually limitless number of possible permutations for visually representing a flow field. So, how does one optimize the output? How can one determine which mappings and what values within each mapping produce the best results? Such optimization requires the ability to rapidly generate high-quality visualizations across a wide variety of parameter mappings and settings. We address this need by providing a highly-configurable interactive software system that allows rapid, human-in-the-loop optimization of two-dimensional flow visualization. This software is then used in a study to generate quality visual solutions to a two-dimensional ocean current flow plus surface temperature over a variety of parameter mappings. The results of this study are used to identify relevant rules and patterns governing the efficacy of each combination of parameters, and to draw some general conclusions concerning 2D flow visualization parameter mapping and values

Dynamic line integral convolution for visualizing electromagnetic phenomena

Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2001.Includes bibliographical references (leaves 62-63).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Vector field visualization is a useful tool in science and engineering, giving us a powerful way of understanding the structure and evolution of the field. A fairly recent technique called Line Integral Convolution (LIC) has improved the level of detail that can be visualized by convolving a random input texture along the streamlines in the vector field. This thesis extends the technique to time-varying vector fields, where the motion of the field lines is specified explicitly via another vector field. The sequence of images generated is temporally coherent, clearly showing the evolution of the fields over time, while at the same time each individual image retains the characteristics of the LIC technique. This thesis describes the new technique, entitled Dynamic Line Integral Convolution, and explores its application to experiments in electromagnetism.by Andreas Sundquist.S.B.M.Eng.and S.B