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.
Type de document :
Communication dans un congrès
Joint Urban Remote Sensing Event, May 2009, Shangai, China. IEEE, pp.1-5, 2009, 〈10.1109/URS.2009.5137562〉
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00833599
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Soumis le : jeudi 13 juin 2013 - 10:32:41
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02

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

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