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Segmentation-based multi-scale edge extraction to measure the persistence of features in unorganized point clouds

Author
Bazazian, D.; Casas, J.; Ruiz-Hidalgo, J.
Type of activity
Presentation of work at congresses
Name of edition
12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Date of publication
2017
Presentation's date
2017-02-27
Book of congress proceedings
Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP
First page
317
Last page
325
DOI
https://doi.org/10.5220/0006092503170325 Open in new window
Repository
http://hdl.handle.net/2117/104304 Open in new window
URL
http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006092503170325 Open in new window
Abstract
Edge extraction has attracted a lot of attention in computer vision. The accuracy of extracting edges in point clouds can be a significant asset for a variety of engineering scenarios. To address these issues, we propose a segmentation-based multi-scale edge extraction technique. In this approach, different regions of a point cloud are segmented by a global analysis according to the geodesic distance. Afterwards, a multi-scale operator is defined according to local neighborhoods. Thereupon, by...
Citation
Bazazian, D., Casas, J., Ruiz-Hidalgo, J. Segmentation-based multi-scale edge extraction to measure the persistence of features in unorganized point clouds. A: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. "12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications". Porto: 2017, p. 317-325.
Keywords
Edge extraction, Multi-scale, Segmentation, Unorganized point cloud
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

Attachments