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Focus + Context Visualization with Animation

Authors: Yingcai Wu; Huamin Qu; Hong Zhou 0004; Ming-Yuen Chan;

Focus + Context Visualization with Animation

Abstract

In this paper, we present some novel animation techniques to help users understand complex structures contained in volumetric data from the medical imaging and scientific simulation fields. Because of the occlusion of 3D objects, these complex structures and their 3D relationships usually cannot be revealed using one image. By our animation techniques, the focus regions, the context regions, and their relationships can be better visualized at the different stages of an animation. We propose an image-centric method which employs layered-depth images (LDIs) to get correct depth cues, and a data-centric method which exploits a novel transfer function fusing technique to guarantee the smooth transition between frames. The experimental results on real volume data demonstrate the advantages of our techniques over traditional image blending and transfer function interpolation methods.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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