Paper Info
Title
Importance-driven focus of attention.
Abstract
This paper introduces a concept for automatic focusing on features within a volumetric data set. The user selects a focus, i.e., object of interest, from a set of pre-defined features. Our system automatically determines the most expressive view on this feature. A characteristic viewpoint is estimated by a novel information-theoretic framework which is based on the mutual information measure. Viewpoints change smoothly by switching the focus from one feature to another one. This mechanism is controlled by changes in the importance distribution among features in the volume. The highest importance is assigned to the feature in focus. Apart from viewpoint selection, the focusing mechanism also steers visual emphasis by assigning a visually more prominent representation. To allow a clear view on features that are normally occluded by other parts of the volume, the focusing for example incorporates cut-away views.
Year
DOI
Venue
2006
10.1109/TVCG.2006.152
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
expressive view,illustrative visualization,novel information-theoretic framework,highest importance,optimal view- point estimation,clear view,volume visualiza- tion,mutual information measure,focus+context techniques,cut-away view,interacting with volumetric datasets,viewpoint selection,pre-defined feature,characteristic viewpoint,importance-driven focus,importance distribution,point estimation,data visualisation,mutual information,indexing terms
Computer vision,Data visualization,Viewpoints,Computer science,Feature recognition,Workstation,Automatic control,Artificial intelligence,Mutual information,Application software,Volumetric data
Journal
Volume
Issue
ISSN
12
5
1077-2626
Citations 
PageRank 
References 
68
2.69
19
Authors
4
Name
Order
Citations
PageRank
Ivan Viola166042.16
Miquel Feixas263745.61
Mateu Sbert31108123.95
Meister Eduard Gröller427313.36