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 visualization

Synthesis of textures is a very popular and active area of research; the applications and the areas of interest are various and significant. In the last years, much work has been done in order to optimize the synthesis process, speeding up the methods and minimizing the processing errors. Recent efforts in this area are concentrated in producing flexible algorithms and in introducing tools, which augment the texture with artistic effects. In this work we present a novel approach for flexible texture synthesis: starting from an input sample the process generates an arbitrary resolution output image; the characteristics of this output texture are user-defined, as the user can freely choose a force field, determine color variations and add further features. The synthesis process is fully automatic and does not require additional intervention. The resulting outputs can be interpreted as filtered versions of a texture or as being obtained through transfer functions.

Improving Aviation Safety with Information Visualization:

Modelling by example has arisen as a powerful paradigm for reducing the artistic skill required for computer graphics. Instead of relying on the user’s own modelling skills, a system that models by example allows users to reuse the work of others. To date, modelling by example, also known as data-driven modelling, has been mostly limited to the image domain. In this work, we develop a number of methods for modelling 3D objects by example. Jump maps provide fast and flexible reuse of texture imagery in object modelling. Geodesic fans extend the local statistical techniques forming the basis for traditional image-based data-driven methods to 3D surfaces, and directly enable flexible reuse of existing 3D surfaces. We also apply data-driven methods to augment surface editing capabilities, providing new tools for rapid geometry or texture editing and sketch-based 3D object modelling. Finally, as a fundamental operation of any data-driven modelling system is the selection of example data, we develop novel methods for selecting regions or mattes from 3D objects. The resulting methods are very fast, intuitive, and easy to use, and, as selection is a truly fundamental modelling operation, have wide applicability. Thus, we