Graph‐Based Wavelet Representation of Multi‐Variate Terrain Data
| dc.contributor.author | Cioaca, Teodor | en_US |
| dc.contributor.author | Dumitrescu, Bogdan | en_US |
| dc.contributor.author | Stupariu, Mihai‐Sorin | en_US |
| dc.contributor.editor | Chen, Min and Zhang, Hao (Richard) | en_US |
| dc.date.accessioned | 2016-03-01T14:13:08Z | |
| dc.date.available | 2016-03-01T14:13:08Z | |
| dc.date.issued | 2016 | en_US |
| dc.description.abstract | Terrain data can be processed from the double perspective of computer graphics and graph theory. We propose a hybrid method that uses geometrical and vertex attribute information to construct a weighted graph reflecting the variability of the vertex data. As a planar graph, a generic terrain data set is subjected to a geometry‐sensitive vertex partitioning procedure. Through the use of a combined, thin‐plate energy and multi‐dimensional quadric metric error, feature estimation heuristic, we construct ‘even’ and ‘odd’ node subsets. Using an invertible lifting scheme, adapted from generic weighted graphs, detail vectors are extracted and used to recover or filter the node information. The design of the prediction and update filters improves the root mean squared error of the signal over general graph‐based approaches. As a key property of this design, preserving the mean of the graph signal becomes essential for decreasing the error measure and conserving the salient shape features.Terrain data can be processed from the double perspective of computer graphics and graph theory. We propose a hybrid method that uses geometrical and vertex attribute information to construct a weighted graph reflecting the variability of the vertex data. As a planar graph, a generic terrain data set is subjected to a geometry‐sensitive vertex partitioning procedure. Through the use of a combined, thin‐plate energy and multi‐dimensional quadric metric error, feature estimation heuristic, we construct ‘even’ and ‘odd’ node subsets. A critically‐sampled lifting scheme design, adapted from generic weighted graphs, is employed to downsample the input. The resulting detail vectors are stored for use in synthesis or filtering applications. | en_US |
| dc.description.number | 1 | en_US |
| dc.description.sectionheaders | Articles | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.description.volume | 35 | en_US |
| dc.identifier.doi | 10.1111/cgf.12670 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12670 | en_US |
| dc.publisher | Copyright © 2016 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | wavelets | en_US |
| dc.subject | methods and applications | en_US |
| dc.subject | digital geometry processing | en_US |
| dc.subject | modeling | en_US |
| dc.subject | level of detail algorithms | en_US |
| dc.subject | modeling | en_US |
| dc.subject | I.3.5 [Computer Graphics]: Computational Geometry and Object Modellinga Curve | en_US |
| dc.subject | surface | en_US |
| dc.subject | solid and object representations | en_US |
| dc.title | Graph‐Based Wavelet Representation of Multi‐Variate Terrain Data | en_US |