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    Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

    236714_Nurunnabi 1year 2014.pdf (8.161Mb)
    Access Status
    Open access
    Authors
    Nurunnabi, Abdul Awal Md.
    Date
    2014
    Supervisor
    Prof. Geoff West
    Dr David Belton
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/543
    Collection
    • Curtin Theses
    Abstract

    Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction.

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      Three dimensional point cloud data obtained from mobile laser scanning systems commonly contain outliers. In the presence of outliers most of the currently used methods such as principal component analysis for point cloud ...
    • Robust and Diagnostic Statistics: A Few Basic Concepts in Mobile Mapping Point Cloud Data Analysis
      Nurunnabi, A.; Belton, David; West, Geoff (2012)
      It is impractical to imagine point cloud data obtained from laser scanner based mobile mapping systems without outliers. The presence of outliers affects the most often used classical statistical techniques used in laser ...
    • Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data
      Nurunnabi, A.; West, Geoff; Belton, David (2015)
      This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust ...
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