Use of multiple low level features to find interesting regions
Vehicle-based mobile mapping systems capture co-registered imagery and 3D point cloud information over hundreds of kilometres of transport corridor. Methods for extracting information from these large datasets are labour intensive and automatic methods are desired. In addition, such methods need to...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/8999 |
| _version_ | 1848745823887687680 |
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| author | Borck, M. West, Geoff Tan, T. |
| author_facet | Borck, M. West, Geoff Tan, T. |
| author_sort | Borck, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Vehicle-based mobile mapping systems capture co-registered imagery and 3D point cloud information over hundreds of kilometres of transport corridor. Methods for extracting information from these large datasets are labour intensive and automatic methods are desired. In addition, such methods need to be easily configured by non-expert users to detect and measure many classes of objects. This paper describes a workflow to take a large number of image and depth features, use machine learning to generate an object detection system that is fast to configure and run. The output is high detection of the objects of interest but with an acceptable number of false alarms. This is desirable as the output is fed into a more complex and hence more computationally expensive analysis system to reject the false alarms and measure the remaining objects. Image and depth features from bounding boxes around objects of interest and random background are used for training with some popular learning algorithms. The interface allows a non-expert user to observe the performance and make modifications to improve the performance. Copyright © 2014 SCITEPRESS. |
| first_indexed | 2025-11-14T06:23:29Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8999 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:23:29Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-89992017-01-30T11:09:57Z Use of multiple low level features to find interesting regions Borck, M. West, Geoff Tan, T. Vehicle-based mobile mapping systems capture co-registered imagery and 3D point cloud information over hundreds of kilometres of transport corridor. Methods for extracting information from these large datasets are labour intensive and automatic methods are desired. In addition, such methods need to be easily configured by non-expert users to detect and measure many classes of objects. This paper describes a workflow to take a large number of image and depth features, use machine learning to generate an object detection system that is fast to configure and run. The output is high detection of the objects of interest but with an acceptable number of false alarms. This is desirable as the output is fed into a more complex and hence more computationally expensive analysis system to reject the false alarms and measure the remaining objects. Image and depth features from bounding boxes around objects of interest and random background are used for training with some popular learning algorithms. The interface allows a non-expert user to observe the performance and make modifications to improve the performance. Copyright © 2014 SCITEPRESS. 2014 Conference Paper http://hdl.handle.net/20.500.11937/8999 fulltext |
| spellingShingle | Borck, M. West, Geoff Tan, T. Use of multiple low level features to find interesting regions |
| title | Use of multiple low level features to find interesting regions |
| title_full | Use of multiple low level features to find interesting regions |
| title_fullStr | Use of multiple low level features to find interesting regions |
| title_full_unstemmed | Use of multiple low level features to find interesting regions |
| title_short | Use of multiple low level features to find interesting regions |
| title_sort | use of multiple low level features to find interesting regions |
| url | http://hdl.handle.net/20.500.11937/8999 |