Course ISE332
Title Introduction to Visualization
Credits 3
Course Coordinator

Klaus Mueller


This course is an introduction to both the foundations and applications of visualization and visual analytics, for the purpose of understanding complex data in science, medicine, business, finance, and many others. It will begin with the basics - visual perception, cognition, human-computer interaction, the sense-making process, data mining, computer graphics, and information visualization. It will then move to discuss how these elementary techniques are coupled into an effective visual analytics pipeline that allows humans to interactively think with data and gain insight. Students will get hands-on experience via several programming projects, using popular public-domain statistics and visualization libraries and APIs. This course is offered as both CSE 332 and ISE 332.

Prerequisite CSE 214 or CSE 260; MAT 211 or AMS 210; AMS 310
Course Outcomes
  • An ability to transform numerical datasets from science and medicine into understandable visual representations.
  • An understanding of the 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.
  • Working knowledge of 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

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

Laboratory Projects
  • 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)

Course Webpage

Crosslisted with CSE332