Name
Abstract | ||
|---|---|---|
Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TVCG.2008.159 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
relation-aware volume,graph interface,scientific visualization,visualization process,volumetric data,relation-aware volume exploration pipeline,relation graph,interactive exploration,rendering (computer graphics),rendered images,index terms—,visualization pipeline.,volume exploration,spatial relation,relation-based visualization,exploratory visualization,data visualisation,region connection calculus,exploration pipeline,graph theory,novel relation-aware visualization pipeline,indexing terms,pipelines,color,data acquisition,data visualization,data mining,calculus | Graph theory,Spatial relation,Computer vision,Pipeline transport,Data visualization,Information visualization,Visualization,Computer science,Theoretical computer science,Artificial intelligence,Scientific visualization,Region connection calculus | Journal |
Volume | Issue | ISSN |
14 | 6 | 1077-2626 |
Citations | PageRank | References |
8 | 0.50 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yuen Chan | 1 | 93 | 6.71 |
| Huamin Qu | 2 | 2033 | 115.33 |
| Ka-Kei Chung | 3 | 20 | 1.77 |
| Wai-Ho Mak | 4 | 55 | 3.44 |
| Yingcai Wu | 5 | 1223 | 61.26 |