Vahl, Matthias ; Lukas, Uwe von ; Urban, Bodo ; Kuijper, Arjan (2015)
Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images.
VISAPP 2015 : 10th International Conference on Computer Vision Theory and Applications. Berlin, Germany (Mar 11, 2015 - Mar 14, 2015)
doi: 10.5220/0005301501000107
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Geo-referenced aerial images are available in very high resolution. The automated production and updating of electronic nautical charts (ENC), as well as other products (e.g. thematic maps), from aerial images is a current challenge for hydrographic organizations. Often standard vision algorithms are not reliable enough for robust object detection in natural images. We thus propose a procedure that combines processing steps on three levels, from pixel (low-level) via segments (mid-level) to semantic information (high level). We combine simple linear iterative clustering (SLIC) as an efficient low-level algorithm with a classification based on texture features by supported vector machine (SVM) and a generalized Hough transformation (GHT) for detecting shapes on mid-level. Finally, we show how semantic information can be used to improve results from the earlier processing steps in the high-level step. As standard vision methods are typically much too slow for such huge-sized images and additionally geographical references must be maintained over the complete procedure, we present a solution to overcome these problems.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Vahl, Matthias ; Lukas, Uwe von ; Urban, Bodo ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images |
| Sprache: | Englisch |
| Publikationsjahr: | März 2015 |
| Verlag: | SciTePress |
| Veranstaltungstitel: | VISAPP 2015 : 10th International Conference on Computer Vision Theory and Applications |
| Veranstaltungsort: | Berlin, Germany |
| Veranstaltungsdatum: | Mar 11, 2015 - Mar 14, 2015 |
| DOI: | 10.5220/0005301501000107 |
| Kurzbeschreibung (Abstract): | Geo-referenced aerial images are available in very high resolution. The automated production and updating of electronic nautical charts (ENC), as well as other products (e.g. thematic maps), from aerial images is a current challenge for hydrographic organizations. Often standard vision algorithms are not reliable enough for robust object detection in natural images. We thus propose a procedure that combines processing steps on three levels, from pixel (low-level) via segments (mid-level) to semantic information (high level). We combine simple linear iterative clustering (SLIC) as an efficient low-level algorithm with a classification based on texture features by supported vector machine (SVM) and a generalized Hough transformation (GHT) for detecting shapes on mid-level. Finally, we show how semantic information can be used to improve results from the earlier processing steps in the high-level step. As standard vision methods are typically much too slow for such huge-sized images and additionally geographical references must be maintained over the complete procedure, we present a solution to overcome these problems. |
| Freie Schlagworte: | Business Field: Digital society, Research Area: Computer vision (CV), Computer vision, Geographic information systems (GIS), Segmentation, Image analysis, Aerial images |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 08 Mai 2019 07:08 |
| Letzte Änderung: | 08 Mai 2019 07:08 |
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