Class overview
The course covers the following topics:
Definitions
- aims and scope of data visualization
- types of visualization (SciVis, InfoVis, SoftVis)
- visualization vs computer graphics
The Visualization Pipeline
- definition and steps
- importance of mapping and inverse mapping
- implementation issues
Data Representation
- from continuous to discrete data and back
- interpolation functions and techniques
- grid types (uniform, structured, rectilinear, unstructured)
- cell types (1D, 2D, 3D)
- supersampling, resampling, computing derivatives
Scalar visualization
- color mapping and colormap design issues
- height and warp plots
- contouring (2D and 3D)
- slicing
Vector visualization
- directional color coding
- vector glyphs
- streamlines and related stream objects
- vector field decomposition
Tensor visualization
- tensor definition
- principal component analysis
- per-component visualization
- anisotropy visualization
- tensor glyphs
- hyperstreamlines
Volume visualization
- relation to scalar visualization
- object-order volume rendering
- image-order volume rendering
- transfer functions design
Dataset processing algorithms
- scattered point interpolation
- triangulation
- grid segmentation