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Communication dans un congrès

Point cloud segmentation towards urban ground modeling

Abstract : This paper presents a new method for segmentation and interpretation of 3D point clouds from mobile LIDAR data. The main contribution of this work is the automatic detection and classification of artifacts located at the ground level. The detection is based on Top-Hat of hole filling algorithm of range images. Then, several features are extracted from the detected connected components (CCs). Afterward, a stepwise forward variable selection by using Wilk's Lambda criterion is performed. Finally, CCs are classified in four categories (lampposts, pedestrians, cars, the others) by using a SVM machine learning method.
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Contributeur : Bibliothèque Mines Paristech <>
Soumis le : jeudi 13 juin 2013 - 10:32:41
Dernière modification le : jeudi 24 septembre 2020 - 16:38:03



Jorge Hernandez, Beatriz Marcotegui. Point cloud segmentation towards urban ground modeling. Joint Urban Remote Sensing Event, May 2009, Shangai, China. pp.1-5, ⟨10.1109/URS.2009.5137562⟩. ⟨hal-00833599⟩



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