Vector Field Analysis and Visualization through Variational Clustering

Alexander McKenzie, Santiago Lombeyda and Mathieu Desbrun (2005) Vector Field Analysis and Visualization through Variational Clustering. In: Eurographics - IEEE VGTC Symposium on Visualization 2005, 1-3 June, 2005, Leeds, UK. [CaltechCACR:2005.106]

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Abstract

Scientic computing is an increasingly crucial component of research in various disciplines. Despite its potential, exploration of the results is an often laborious task, owing to excessively large and verbose datasets output by typical simulation runs. Several approaches have been proposed to analyze, classify, and simplify such data to facilitate an informative visualization and deeper understanding of the underlying system. However, traditional methods leave much room for improvement. In this article we investigate the visualization of large vector elds, departing from accustomed processing algorithms by casting vector eld simplication as a variational partitioning problem. Adopting an iterative strategy, we introduce the notion of vector ieproxiesln to minimize the distortion error of our simplifiation by clustering the dataset into multiple best-fitting characteristic regions. This error driven approach can be performed with respect to various similarity metrics, offering a convenient set of tools to design clear and succinct representations of high dimensional datasets. We illustrate the benefits of such tools through visualization experiments of three-dimensional vector fields.

EPrint Type:	Conference or Workshop Item (Paper)
Additional Information:	Also available as cit-asci-tr308 at http://csdrm.caltech.edu/publications/index.html We wish to thank Pierre Alliez for providing insight (and examples, as in Figure 3(a)) into new streamline techniques, Peter Schröder, for the generous provision of lab space and Thai food, Céline Loscos for support, Rudiger Westermann for the car dataset, and the Student-Faculty Program at Caltech that made this work possible. This work was funded in part by the NSF (CCR-0133983, DMS-0221666, DMS-0221669, DMS-0453145), the DOE (DE-FG02-04ER25657), and Pixar Animation Studios.
Uncontrolled Keywords:	Computer Graphics, Flow Visualization
Subjects:	All Records
ID Code:	113
Deposited By:	Sarah M. Emery
Deposited On:	08 February 2006
Record Number:	CaltechCACR:2005.106
Official Persistent URL:	http://resolver.caltech.edu/CaltechCACR:2005.106
Usage Policy:	You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.

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