Scientific Visualization

Course logistics, overview of scientific visualization techniques.

The visualization pipeline.

Data types, meshes, and data structures.

Tufte's principles, data tables, examples of plot types. Typical matplotlib program structure, displaying multiple plots, bar charts, pie charts, histograms, and scatter plots.

No class.

1D/2D/3D scalar fields, color mapping, transfer functions, interpolation (linear and bilinear) , and 2D isocontour ambiguities.

Multi-resolution data formats, data streaming model, interactive analysis and visualization of large scientific data.
[Live tutorial]
[Jupyter Template]

No class, due to weather conditions

Computation of isosurfaces, the marchine cube algorithm, trilinear interpolation, resolution of ambiguities.

Advanced surface visualization, scalability, acceleration data structures, view dependent data reduction.

Present solution for first homework and discussions

No class.

Volume rendering, compositing, modeling light, ray casting.

Rendering primitives, shading, orthographic vs. perspective projections

Rasterization, illumination, non-photorealistic rendering.

Solution second homework

Transfer functions for 3D data, volume classification, multi-dimensional transfer functions.

No class.

No class.

Introduction to vector fields, 2D vector field representation, integration methods, 4th order Runge-Kutta method.

Seeding streamlines, Line Integral of Convolution (LIC), 3D LIC, illumination and depth cues.

Tensor in engineering and medicine, diffusion tensor imaging, eigenvalues and eigenvectors, direct visualization methods.

Cognitive neuroscience, optical illusions, the human visual system, Cornsweet Illusion.

CIE Chromaticity Diagram, color spaces, color deficiencies, patterns, continuty, and closure.

The cave and other immersive enviroments, tasks benefiting from VR, applications in neuroscience and metrology.

Visualization of error and uncertaintly, the boxplot and its modifications, topological uncertainty.

Isosurface uncertainty, volume rendering uncertainty, ensemble curved boxplot.

The four V's of big data, isosurfaces of big data, data layouts for visualization of big data experiemnts and simulations.

Multi-resolution data structures, parallel visualization, image compositing for volume rendering.

In situ analysis and visualization, post hoc data exploration, data quality, and data reduction.