- roleTerm ( text )
- advisor
- namePart
- Nel, A.L., Prof.
- roleTerm ( text )
- author
- namePart
- Cronje, Jaco
- dateAccessioned
- 2012-10-24T05:58:33Z
- dateAvailable
- 2012-10-24T05:58:33Z
- dateIssued
- 2012-10-24
- dateSubmitted
- 2012-10-15
- identifier ( uri )
- http://hdl.handle.net/10210/7881
- note
- M.Phil.
- abstract
- This work proposes a fast local image feature detector and descriptor that is im- plementable on a GPU. The BFROST feature detector is the first published GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of the orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, the BFROST feature descriptor is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory usage. It is demonstrated that BFROST is usable in real-time applications such as vision-based localization and mapping of images captured from micro aerial platforms.
- languageTerm ( rfc3066 )
- en
- topic
- Binary image
- topic
- Micro aerial vehicle captured images
- topic
- Remote sensing - Data processing
- topic
- Micro air vehicles
- topic
- Mobile geographic information systems
- topic
- Imaging systems
- title
- Binary image features designed towards vision-based localization and environment mapping from micro aerial vehicle (MAV) captured images
- genre
- Thesis