CSE332

CSE332

Introduction to Scientific Visualization

3

Klaus Mueller

Visualization of scientific, engineering, medical, and business data sets. Mechanisms to acquire sampled, computed, or synthetic data and methods to transform symbolic into the visual. Topics include classic visualization process; visual perception; volume and surface visualization; methods for visualizing sampled, simulated, and geometric objects; and visualization systems. Emphasis on applications and case studies.

This course is offered as both CSE 332 and ISE 332.

CSE 114; MAT 211 or AMS 210

• Demonstrate how to transform numerical datasets from science and medicine into understandable visual representations.
• Understand issues associated with digital image quality (e.g. sampling artifacts) and algorithms for performing basic image manipulation operations such as filtering, re-sampling, and intensity transformation.
• Investigate methods (including graphical user interfaces) for the visualization of three-dimensional data sets.

Computer Graphics: Principles and Practice 2 edition in C, J.D. Foley, A. van Dam, S.K. Feiner, J.F. Hughes, Addison-Wesley, 1995

Introduction to Volume Rendering Lichtenbelt, R. Crane, S. Naqvi Prentice-Hall, 1998

• Visualization: purpose, history, examples (1 week)
• Perception, color, color models (1 week)
• Information visualization (1 week)
• Image processing (1 week)
• Sampling theory, anti-aliasing, filtering, interpolation, image magnification and minification (1 week)
• Graphical user interface design, the FLTK GUI toolkit (0.5 week)
• Geometric transformations, viewing transforms, the 3D graphics pipeline (1 week)
• X-ray rendering, Maximum Intensity Projection rendering, raycasting (1 week)
• Shading, illumination, lighting models, classification. segmentation, transfer functions, mapping of data to color and opacity (1 week)
• The volume rendering pipeline, iso-surface rendering, full volume rendering, semi-transparent rendering (1 week)
• Polygonal rendering with shading, extraction of polygonal models from sampled volume data, Marching Cubes, introduction to OpenGL (1 week)
• Generation of volume data: medical scanning (0.5 week)
• Visualization of vector field data: streamlines, ribbons, icons, glyphs (1 week)

• Image processing: mask-based filtering, median filtering, subsampling, edge-detection (2 weeks)
• Basic volume rendering; X-ray, maximum-intensity projection, viewing transformations, magnification and zoom (2 weeks)
• Advanced volume rendering: rendering with shading and lighting effects, rendering with different levels of semin-transparencies, transfer functions mapping density to color and transparency (2 weeks)
• Polygonal rendering: extraction of iso-surfaces with Marching Cubes and polygonal display (extra credit 2 weeks)

http://www.cs.sunysb.edu/~cse332