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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/806

Title: Level set and PDE methods for visualization
Authors: Breen, David E.
Kirby, Mike
Lefohn, Aaron
Museth, Ken
Preusser, Tobias
Sapiro, Guillermo
Whitaker, Ross
Issue Date: 25-Oct-2005
Citation: Notes from IEEE Visualization 2005 Course #6, Minneapolis, MN, October 25, 2005. Retrieved 3/16/2006 from http://www.cs.drexel.edu/~david/Papers/Viz05_Course6_Notes.pdf.
Abstract: Level set methods, an important class of partial differential equation (PDE) methods, define dynamic surfaces implicitly as the level set (isosurface) of a sampled, evolving nD function. This course is targeted for researchers interested in learning about level set and other PDE-based methods, and their application to visualization. The course material will be presented by several of the recognized experts in the field, and will include introductory concepts, practical considerations and extensive details on a variety of level set/PDE applications. The course will begin with preparatory material that introduces the concept of using partial differential equations to solve problems in visualization. This will include the structure and behavior of several different types of differential equations, e.g. the level set, heat and reaction-diffusion equations, as well as a general approach to developing PDE-based applications. The second stage of the course will describe the numerical methods and algorithms needed to implement the mathematics and methods presented in the first stage, including information on implementing the algorithms on GPUs. Throughout the course the technical material will be tied to applications, e.g. image processing, geometric modeling, dataset segmentation, model processing, surface reconstruction, anisotropic geometric diffusion, flow field post-processing and vector visualization. Prerequisites: Knowledge of calculus, linear algebra, computer graphics, visualization, geometric modeling and computer vision. Some familiarity with differential geometry, differential equations, numerical computing and image processing is strongly recommended, but not required.
URI: http://hdl.handle.net/1860/806
Appears in Collections:Faculty Research and Publications (Comp Sci)

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