Abstract
Many modern autonomous systems use disparity maps for recognition and interpretation of their environment. The depth information of these disparity maps can be utilised for point cloud generation. Real-time and high-quality processing of point clouds is necessary for reliable detection of safety-relevant issues such as barriers or obstacles in road traffic. However, quality characteristics of point clouds are influenced by properties of depth sensors and environmental conditions such as illumination, surface and texture. Quality optimisation and real-time implementation can be resource intensive. Limiting the amount of data allows optimisation of real-time processing. We use Kohonen network existing self-organising maps to identify and segment salient objects in disparity maps. Kohonen networks use unsupervised learning to generate disparity maps abstracted by a small number of vectors instead of all pixels. The combination of object-specific segmentation and reduced pixel number decreases the memory and processing time towards real-time compatibility. Our results show that trained self-organising maps can be applied to disparity maps for improved runtime, reduced data volume and further processing of 3D reconstruction of salient objects.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Keywords: | Kohonen Networks, Self-Organising Maps, Depth Image Segmentation, Disparity Maps, Computer Vision |
Fakultät: | Mathematik, Informatik und Statistik > Informatik |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik |
URN: | urn:nbn:de:bvb:19-epub-93049-4 |
Sprache: | Englisch |
Dokumenten ID: | 93049 |
Datum der Veröffentlichung auf Open Access LMU: | 17. Okt. 2024 08:14 |
Letzte Änderungen: | 17. Okt. 2024 08:31 |