Feature Extraction from Multi-modal Mobile Mapping Data
This thesis investigates many different feature extraction methods and machine learning algorithms for their usefulness in detecting objects from vehicle-based mobile mapping systems datasets. A comprehensive analysis using performances measures and graphical techniques are applied to identify the...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/57505 |
| _version_ | 1848760060458565632 |
|---|---|
| author | Borck, Michael Geoffery |
| author_facet | Borck, Michael Geoffery |
| author_sort | Borck, Michael Geoffery |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis investigates many different feature extraction methods and machine learning algorithms for their usefulness in detecting objects from vehicle-based mobile mapping systems datasets. A comprehensive analysis using performances measures and graphical techniques are applied to identify the best combination of features and classifiers. A system was built enable users who are not programmers to manage image data and to customise their analyses by combining common data analysis tools to fit their needs. |
| first_indexed | 2025-11-14T10:09:46Z |
| format | Thesis |
| id | curtin-20.500.11937-57505 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:09:46Z |
| publishDate | 2016 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-575052017-11-13T08:33:58Z Feature Extraction from Multi-modal Mobile Mapping Data Borck, Michael Geoffery This thesis investigates many different feature extraction methods and machine learning algorithms for their usefulness in detecting objects from vehicle-based mobile mapping systems datasets. A comprehensive analysis using performances measures and graphical techniques are applied to identify the best combination of features and classifiers. A system was built enable users who are not programmers to manage image data and to customise their analyses by combining common data analysis tools to fit their needs. 2016 Thesis http://hdl.handle.net/20.500.11937/57505 Curtin University fulltext |
| spellingShingle | Borck, Michael Geoffery Feature Extraction from Multi-modal Mobile Mapping Data |
| title | Feature Extraction from Multi-modal Mobile Mapping Data |
| title_full | Feature Extraction from Multi-modal Mobile Mapping Data |
| title_fullStr | Feature Extraction from Multi-modal Mobile Mapping Data |
| title_full_unstemmed | Feature Extraction from Multi-modal Mobile Mapping Data |
| title_short | Feature Extraction from Multi-modal Mobile Mapping Data |
| title_sort | feature extraction from multi-modal mobile mapping data |
| url | http://hdl.handle.net/20.500.11937/57505 |