Proceedings IEEE Symposium on Information Visualization '96
DOI: 10.1109/infvis.1996.559215
|View full text |Cite
|
Sign up to set email alerts
|

Distortion viewing techniques for 3-dimensional data

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 16 publications
(6 citation statements)
references
References 10 publications
0
6
0
Order By: Relevance
“…They can recognize the application they are embedded into, critical stages within the workflow, uncertain user behavior calling for help, or occlusion conditions when visualizing dynamic information. Although most of the visualization techniques used in our demo have been previously presented individually by others [7][9] [24][50] [51], their use and combination with the augmentation of physical objects and autonomous agents is novel.…”
Section: Agent Representationmentioning
confidence: 99%
“…They can recognize the application they are embedded into, critical stages within the workflow, uncertain user behavior calling for help, or occlusion conditions when visualizing dynamic information. Although most of the visualization techniques used in our demo have been previously presented individually by others [7][9] [24][50] [51], their use and combination with the augmentation of physical objects and autonomous agents is novel.…”
Section: Agent Representationmentioning
confidence: 99%
“…Carpendale et al 7 give an example of view-dependent distortion visualization used in 3D data. Importance-driven feature enhancement 8 is another way to emphasize the important features based on the viewpoint to cut away occluding objects or render the less important objects sparsely.…”
Section: Related Workmentioning
confidence: 99%
“…Alternatively, the position of the objects may be augmented to facilitate the understanding of the data. For example, Sheelagh et al 9 use this technique to provide a`clear line of sight' to obscured an concealed sections of the data. The abstract views may allow objects that are normally occluded to be seen, or generate a structural view of the whole data.…”
Section: The Conceptsmentioning
confidence: 99%