Robust statistical approaches for feature extraction in laser scanning 3D point cloud data
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...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Curtin University
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/543 |
| _version_ | 1848743408735092736 |
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| author | Nurunnabi, Abdul Awal Md. |
| author_facet | Nurunnabi, Abdul Awal Md. |
| author_sort | Nurunnabi, Abdul Awal Md. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T05:45:06Z |
| format | Thesis |
| id | curtin-20.500.11937-543 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:45:06Z |
| publishDate | 2014 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-5432017-02-20T06:40:02Z Robust statistical approaches for feature extraction in laser scanning 3D point cloud data Nurunnabi, Abdul Awal Md. 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. 2014 Thesis http://hdl.handle.net/20.500.11937/543 en Curtin University fulltext |
| spellingShingle | Nurunnabi, Abdul Awal Md. Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title | Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title_full | Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title_fullStr | Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title_full_unstemmed | Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title_short | Robust statistical approaches for feature extraction in laser scanning 3D point cloud data |
| title_sort | robust statistical approaches for feature extraction in laser scanning 3d point cloud data |
| url | http://hdl.handle.net/20.500.11937/543 |