Binary image features designed towards vision-based localization and environment mapping from micro aerial vehicle (MAV) captured images

DSpace/Manakin Repository

Show simple item record

dc.contributor.advisor Nel, A.L., Prof. en_US
dc.contributor.author Cronje, Jaco
dc.date.accessioned 2012-10-24T05:58:33Z
dc.date.available 2012-10-24T05:58:33Z
dc.date.issued 2012-10-24
dc.date.submitted 2012-10-15
dc.identifier.uri http://hdl.handle.net/10210/7881
dc.description M.Phil. en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.subject Binary image en_US
dc.subject Micro aerial vehicle captured images en_US
dc.subject Remote sensing - Data processing
dc.subject Micro air vehicles
dc.subject Mobile geographic information systems
dc.subject Imaging systems
dc.title Binary image features designed towards vision-based localization and environment mapping from micro aerial vehicle (MAV) captured images en_US
dc.type Thesis en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search UJDigispace


Advanced Search

Browse

My Account