Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds

Dena Bazazian, Josep R. Casas, Javier Ruiz-Hidalgo

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.

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Paper Citation


in Harvard Style

Bazazian D., R. Casas J. and Ruiz-Hidalgo J. (2017). Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 317-325. DOI: 10.5220/0006092503170325


in Bibtex Style

@conference{visapp17,
author={Dena Bazazian and Josep R. Casas and Javier Ruiz-Hidalgo},
title={Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={317-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006092503170325},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds
SN - 978-989-758-225-7
AU - Bazazian D.
AU - R. Casas J.
AU - Ruiz-Hidalgo J.
PY - 2017
SP - 317
EP - 325
DO - 10.5220/0006092503170325