NDLI: A multi-dimensional importance metric for contour tree simplification

Content Provider	Springer Nature Link
Author	Zhou, Jianlong Xiao, Chun Takatsuka, Masahiro
Copyright Year	2013
Abstract	Real-world data sets produce unmanageably large contour trees because of noise. Contour Tree Simplification (CTS) would remove small scale branches, and maintain essential structure of data. Despite multiple measures of importance (MOIs) available, conventional CTS approaches often use a single MOI, which is not enough in evaluating the importance of branches in the CTS. This paper proposes an importance-driven CTS approach. The proposed approach combines multiple MOIs through the introduction of various concepts to maximize advantages of each MOI. In the attribute space, various attributes of a branch are organized in a single space. The concept of the importance triangle is used to evaluate the importance of a branch by size of the importance triangle. It considers the whole attribute space and gives better evaluation of importance. Finally, new importance values of branches are compared in the importance space to make simplification decisions.
Starting Page	341
Ending Page	349
Page Count	9
File Format	PDF
ISSN	13438875
Journal	Journal of Visualization
Volume Number	16
Issue Number	4
e-ISSN	18758975
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Contour tree simplification Attribute space Multi-dimensional Importance-driven Measure of importance Volume rendering Engineering Fluid Dynamics Computer Imaging, Vision, Pattern Recognition and Graphics Classical Continuum Physics Engineering Thermodynamics, Heat and Mass Transfer
Content Type	Text
Resource Type	Article
Subject	Condensed Matter Physics Electrical and Electronic Engineering

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