Generic high level feature detection techniques using multi-modal spatial data
Object and pattern recognition techniques have classically used 2-D images. Mobile-mapping systems produce images with the added modality of depth. This is motivating renewed interest in aspects of object recognition research, especially in relation to issues of scale. This thesis reports on techniq...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
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Curtin University
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/279 |
| _version_ | 1848743333625593856 |
|---|---|
| author | Palmer, Richard Leslie |
| author_facet | Palmer, Richard Leslie |
| author_sort | Palmer, Richard Leslie |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Object and pattern recognition techniques have classically used 2-D images. Mobile-mapping systems produce images with the added modality of depth. This is motivating renewed interest in aspects of object recognition research, especially in relation to issues of scale. This thesis reports on techniques that have been developed to incorporate depth into state-of-the-art 2-D object detection and localisation methods. The techniques are empirically shown to enhance detection accuracy across a range of datasets and object types. |
| first_indexed | 2025-11-14T05:43:54Z |
| format | Thesis |
| id | curtin-20.500.11937-279 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:43:54Z |
| publishDate | 2015 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-2792017-02-20T06:41:36Z Generic high level feature detection techniques using multi-modal spatial data Palmer, Richard Leslie Object and pattern recognition techniques have classically used 2-D images. Mobile-mapping systems produce images with the added modality of depth. This is motivating renewed interest in aspects of object recognition research, especially in relation to issues of scale. This thesis reports on techniques that have been developed to incorporate depth into state-of-the-art 2-D object detection and localisation methods. The techniques are empirically shown to enhance detection accuracy across a range of datasets and object types. 2015 Thesis http://hdl.handle.net/20.500.11937/279 en Curtin University fulltext |
| spellingShingle | Palmer, Richard Leslie Generic high level feature detection techniques using multi-modal spatial data |
| title | Generic high level feature detection techniques using multi-modal spatial data |
| title_full | Generic high level feature detection techniques using multi-modal spatial data |
| title_fullStr | Generic high level feature detection techniques using multi-modal spatial data |
| title_full_unstemmed | Generic high level feature detection techniques using multi-modal spatial data |
| title_short | Generic high level feature detection techniques using multi-modal spatial data |
| title_sort | generic high level feature detection techniques using multi-modal spatial data |
| url | http://hdl.handle.net/20.500.11937/279 |