Name
Abstract | ||
|---|---|---|
The exploration of volumetric datasets is a challenging task due to its three-dimensional nature. Segmenting or classifying the volume helps to reduce the dimensionality of the problem, but there remains the issue of searching through the feature space in order to find regions of interest. This problem is aggravated when the relation between scalar values and spatial features is unclear or unknown. To aid in the identification and selection of significant structures, interactive exploration methods are important, as they help to correlate the volumetric rendering with the scalar data domain. In this work, we present a semi-automatic method for exploring volumetric datasets using a graph-based approach. First, we automatically classify the volume from a 2D histogram, following ideas from previous proposals. Then, through a graph structure with dynamic edge weights, a hierarchy is generated to identify similar structures. The final hierarchy allows for an interactive and in-depth volume exploration by splitting, joining or removing regions in real-time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.cag.2016.06.007 | Computers & Graphics |
Keywords | Field | DocType |
Volume exploration,Graph algorithms | Data mining,Computer vision,Histogram,Volume rendering,Feature vector,Data domain,Market segmentation,Computer science,Scalar (physics),Curse of dimensionality,Artificial intelligence,Hierarchy | Journal |
Volume | ISSN | Citations |
60 | 0097-8493 | 3 |
PageRank | References | Authors |
0.41 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Ponciano | 1 | 3 | 0.41 |
| Marcos Seefelder | 2 | 3 | 0.41 |
| R. Marroquim | 3 | 58 | 9.94 |