Abstract
Feature extraction is an approach to visualization that extracts important regions or objects of interest algorithmically from large data sets. In our feature extraction process, high-level attributes are calculated for the features, thus resulting in averaged quantitative measures. The usability of these measures depends on their robustness with noise and their dependency on parameters like the density of the grid that is used. In this paper experiments are described to investigate the accuracy and robustness of the feature extraction method. Synthetic data is generated with predefined features, this data is used in the feature extraction procedure, and the obtained attributes of the feature are compared to the input attributes. This has been done for several grid resolutions, for different noise levels, and with different feature extraction parameters. We present the results of the experiments, and also derive a number of guidelines for setting the extraction parameters.
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References
D. C. Banks, B. A. Singer. A predictor-corrector technique for visualizing unsteady flow. IEEE Trans, on Visualization and Computer Graphics, 1 (2): 151–163, June 1995.
J. L. Helman, L. Hesselink. Visualization vector field topology in fluid flows. IEEE Computer Graphics and Applications, 11 (3): 36–46, 1991.
H. G. Pagendarm, B. Seitz. An algorithm for detection and visualization of discontinuities in scientific data fields applied to flow data with shock waves. In P. Palamidese, editor, Scientific Visualization: Advanced Software Techniques, pages 161–177. Ellis Horwood Limited, 1993.
F. J. Post, T. van Walsum, F. H. Post, D. Silver. Iconic techniques for feature visualization. In G.M. Nielson, D. Silver, editors, Proc. Visualization ‘95, pages 288–295. IEEE Computer Society Press, 1995.
W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, second edition, 1992.
F. Reinders, F. H. Post, H. J. W. Spoelder. Feature extraction from pioneer venus ocpp data. In W. Lefer, M. Grave, editors, Visualization in Scientific Computing ‘97, pages 85–94. Springer Verlag, April 1997.
D. Silver, N. J. Zabusky. Quantifying visualizations for reduced modeling in nonlinear science: Extracting structures from datasets. J. of Visual Communication and Image Presentation, 4 (1): 46–61, March 1993.
T. van Walsum, F. H. Post, D. Silver, F. J. Post. Feature extraction and iconic visualization. Trans, on Visualization and Computer Graphics, 2 (2): 111–119, 1996.
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© 1998 Springer-Verlag/Wien
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Reinders, F., Spoelder, H.J.W., Post, F.H. (1998). Experiments on the Accuracy of Feature Extraction. In: Bartz, D. (eds) Visualization in Scientific Computing ’98. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7517-0_5
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DOI: https://doi.org/10.1007/978-3-7091-7517-0_5
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83209-7
Online ISBN: 978-3-7091-7517-0
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