Feature identification and extraction in function fields

Authors:
John C. Anderson;Luke J. Gosink;Mark A. Duchaineau;Kenneth I. Joy
Affiliations:
Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis;Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory;Institute for Data Analysis and Visualization, Department of Computer Science, University of California, Davis
Venue:
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Year:
2007

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Abstract

We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is ac- complished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.