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David Belton
Researcher at Curtin University
Publications - 54
Citations - 1159
David Belton is an academic researcher from Curtin University. The author has contributed to research in topics: Point cloud & RANSAC. The author has an hindex of 16, co-authored 54 publications receiving 907 citations. Previous affiliations of David Belton include Cooperative Research Centre & CRC for Spatial information.
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Journal ArticleDOI
Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data
TL;DR: 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, based on a robust z-score and a Mahalanobis type robust distance are proposed.
Proceedings ArticleDOI
Robust Segmentation in Laser Scanning 3D Point Cloud Data
TL;DR: Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation.
Journal ArticleDOI
Early human occupation of a maritime desert, Barrow Island, North-West Australia
Peter Veth,Ingrid Ward,Tiina Manne,Sean Ulm,Kane Ditchfield,Joe Dortch,Fiona Hook,Fiona Petchey,Alan G. Hogg,Daniele Questiaux,Martina Demuro,Lee J. Arnold,Nigel A. Spooner,Vladimir Levchenko,Jane Skippington,Chae Byrne,Mark Basgall,David Zeanah,David Belton,Petra Helmholz,Szilvia Bajkan,Richard M. Bailey,Christa Placzek,Peter Kendrick +23 more
TL;DR: In this article, the authors focus on the dating and sedimentology of Boodie Cave to establish the framework for ongoing analysis of cultural materials and present new data on these cultural assemblages, including charcoal, faunal remains and lithics.
Journal ArticleDOI
Robust statistical approaches for local planar surface fitting in 3D laser scanning data
TL;DR: The proposed robust methods, called DetRD-PCA and DetRPCA, are significantly more efficient, faster, and produce more accurate fits and robust local statistics, necessary for many point cloud processing tasks.
Journal ArticleDOI
Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data
TL;DR: Experiments show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data and is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems.