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)