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dc.contributor.authorNurunnabi, Abdul Awal Md.
dc.contributor.supervisorProf. Geoff West
dc.contributor.supervisorDr David Belton

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.

dc.publisherCurtin University
dc.titleRobust statistical approaches for feature extraction in laser scanning 3D point cloud data
curtin.departmentDepartment of Spatial Sciences
curtin.accessStatusOpen access

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