Directional enhancement in texture-based vector field visualization
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Dense texture-based visualization of unsteady and multi-variate vector fields
Computers and Graphics
Visualization of Advection-Diffusion in Unsteady Fluid Flow
Computer Graphics Forum
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Vector field visualization aims at generating images in order to convey the information existing in the data. We use Markov Random Field (MRF) texture synthesis methods to generate the visualization from a set of sample textures. MRF texture synthesis methods allow generating images that are locally similar to a given example image. We extend this idea for vector field visualization by identifying each vector value with a representative example image, e.g. a strongly directed texture that is rotated according to a 2D vector. The visualization is synthesized pixel by pixel, where each pixel is chosen from the sample texture according to the vector values of the local pixel. The visualization locally communicates the vector information as each pixel is chosen from a sample that is representative of the vector. Furthermore it is smooth, as MRF texture synthesis searches for best fitting neighborhoods. This leads to dense and smooth visualizations with the additional freedom to use arbitrary representation textures for any vector value.