Name
Abstract | ||
|---|---|---|
An algorithm is presented that allows one to perform skeletonization of large maps with much lower memory requirements than with the straightforward approach. The maps are divided into overlapping tiles, which are skeletonized separately, using a Euclidean distance transform. The amount of overlap is controlled by the maximum expected width of any map component and the maximum size of what is considered as a small component. Next, the skeleton parts are connected again at the middle of the overlap zones. Some examples are given for efficient memory utilization in tiling an A0 size map into a predefined number of tiles or into tiles of a predefined (square) size. The algorithm is also suited for a parallel implementation of skeletonization. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/ICDAR.1997.620620 | ICDAR-1 |
Keywords | Field | DocType |
physics,skeletonization,geographic information systems,image recognition,euclidean distance,algorithm design and analysis,cartography,image processing,pattern recognition,skeleton | Euclidean distance transform,Computer vision,Pattern recognition,Computer science,Image processing,Skeletonization,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
2 | 1520-5363 | 0-8186-7898-4 |
Citations | PageRank | References |
3 | 0.64 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert M. Vossepoel | 1 | 126 | 18.43 |
| Klamer Schutte | 2 | 173 | 18.26 |
| Carl F. P. Delanghe | 3 | 3 | 0.64 |