Boundary aware reconstruction of scalar fields

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Publication Type pre-print
School or College College of Engineering
Department Computing, School of
Creator Hansen, Charles D.
Other Author Lindholm, Stefan; Jonsson, Daniel; Ynnerman, Anders
Title Boundary aware reconstruction of scalar fields
Date 2014-01-01
Description In visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials' local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user's understanding of discrete features within the studied object.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 20
Issue 12
First Page 2447
Last Page 2455
Language eng
Bibliographic Citation Lindholm, S., Jonsson, D., Hansen, C., & Ynnerman, A. (2014). Boundary aware reconstruction of scalar fields. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2447-55.
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Format Medium application/pdf
Format Extent 905,224 bytes
Identifier uspace,19084
ARK ark:/87278/s6vh8xz6
Setname ir_uspace
Date Created 2014-12-15
Date Modified 2021-05-06
ID 712751
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