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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5387

Title: BFROST: binary features from robust orientation segment tests accelerated on the GPU
Authors: Cronje, J
Keywords: Computer vision
Feature detection
Feature extraction
FAST detector
BFROST
Pattern recognition association
PRASA 2011
Issue Date: Nov-2011
Citation: Cronje, J. 2011. BFROST: binary features from robust orientation segment tests accelerated on the GPU. 22nd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Emerald Casino and Resort, Vanderbijlpark, South Africa, 22-25 November 2011
Series/Report no.: Workflow request;7659
Abstract: A fast local image feature detector and descriptor that is implementable on the GPU is proposed. This method is the first 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 our orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, a binary feature descriptor is proposed which is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory consumption. The proposed method demonstrates good robustness and very fast computation times, making it usable in real-time applications.
Description: 22nd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Emerald Casino and Resort, Vanderbijlpark, South Africa, 22-25 November 2011
URI: http://hdl.handle.net/10204/5387
Appears in Collections:Optronic sensor systems
Digital intelligence
General science, engineering & technology

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