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

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10.5220/0006092503170325
 
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Bazazian, Dena
Casas Pla, Josep RamonMés informacióMés informacióMés informació
Ruiz Hidalgo, JavierMés informacióMés informacióMés informació
Document typeConference report
Defense date2017
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
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 applying this operator at multiple scales of the point cloud, the persistence of features is determined. We illustrate the proposed method by computing a feature weight that measures the likelihood of a point to be an edge, then detects the edge points based on that value at both global and local scales. Moreover, we evaluate quantitatively and qualitatively our method. Experimental results show that the proposed approach achieves a superior accuracy. Furthermore, we demonstrate the robustness of our approach in noisier real-world datasets.
CitationBazazian, 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. 
URIhttp://hdl.handle.net/2117/104304
DOI10.5220/0006092503170325
ISBN978-989-758-225-7
Publisher versionhttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006092503170325
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