Name
Abstract | ||
|---|---|---|
In this paper, we present a study on skeletonization of real-world shape data. The data stem from the cultural heritage domain and represent contact tracings of prehistoric petroglyphs. Automated analysis can support the work of archeologists on the investigation and categorization of petroglyphs. One strategy to describe petroglyph shapes is skeleton-based. The skeletonization of petroglyphs is challenging since their shapes are complex, contain numerous holes and are often incomplete or disconnected. Thus they pose an interesting testbed for skeletonization. We present a large real-world dataset consisting of more than 1100 petroglyph shapes. We investigate their properties and requirements for the purpose of skeletonization, and evaluate the applicability of state-of-the-art skeletonization and skeleton pruning algorithms on this type of data. Experiments show that pre-processing of the shapes is crucial to obtain robust skeletons. We propose an adaptive pre-processing method for petroglyph shapes and improve several state-of-the-art skeletonization algorithms to make them suitable for the complex material. Evaluations on our dataset show that 79.8 % of all shapes can be improved by the proposed pre-processing techniques and are thus better suited for subsequent skeletonization. Furthermore we observe that a thinning of the shapes produces robust skeletons for 83.5 % of our shapes and outperforms more sophisticated skeletonization techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-3395-1 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Skeletonization, Petroglyphs, Shape pre-processing, Real-world shape data | Computer vision,Skeleton pruning,Cultural heritage,Pattern recognition,Computer science,Skeletonization,Artificial intelligence,Petroglyph | Journal |
Volume | Issue | ISSN |
76 | 6 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ewald Wieser | 1 | 8 | 1.17 |
| Markus Seidl | 2 | 30 | 4.36 |
| Matthias Zeppelzauer | 3 | 186 | 21.35 |