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

Dena Bazazian, Josep R. Casas, Javier Ruiz-Hidalgo
2017 Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
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
more » ... pplying 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. 317 unorganized point clouds. The remainder of the article is organized as follows. Section 2 presents related work, followed by a description of our approach and architecture in Section 3. Section 4 reports the experimental results of our approach, and conclusions are drawn in section 5.
doi:10.5220/0006092503170325 dblp:conf/visapp/BazazianCH17 fatcat:kgevjafkqnc57iehjivcfsq5fa