Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU
(2016) 5th International Conference on Pattern Recognition Applications and Methods (IPCRAM 2016) p.517-525- Abstract
- GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. The implementation is done in OpenCL, and we evaluate the execution time and classification performance. We compare our approach with two other methods, FAST/BRISK and ORB. Performance data is presented for the mobile device Xperia Z3 and the desktop Nvidia GTX 660. Our results indicate that the execution times on the Xperia Z3 are insufficient for real-time applications while desktop execution shows future potential.... (More)
- GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. The implementation is done in OpenCL, and we evaluate the execution time and classification performance. We compare our approach with two other methods, FAST/BRISK and ORB. Performance data is presented for the mobile device Xperia Z3 and the desktop Nvidia GTX 660. Our results indicate that the execution times on the Xperia Z3 are insufficient for real-time applications while desktop execution shows future potential. Classification performance of Harris-Hessian/FREAK indicates that the solution is sensitive to rotation, but superior in scale variant images. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/eef9fdb0-fbe5-45cb-9300-ef7405d18f84
- author
- Danielsson, Max ; Sievert, Thomas ; Grahn, Håkan and Ramusson, Jim
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
- pages
- 517 - 525
- publisher
- SciTePress
- conference name
- 5th International Conference on Pattern Recognition Applications and Methods (IPCRAM 2016)
- conference location
- Rome, Italy
- conference dates
- 2016-02-24 - 2016-02-26
- external identifiers
-
- scopus:84969931851
- ISBN
- 978-989-758-173-1
- DOI
- 10.5220/0005662005170525
- project
- Embedded Applications Software Engineering
- language
- English
- LU publication?
- no
- id
- eef9fdb0-fbe5-45cb-9300-ef7405d18f84
- date added to LUP
- 2018-09-25 11:47:12
- date last changed
- 2024-01-15 01:56:45
@inproceedings{eef9fdb0-fbe5-45cb-9300-ef7405d18f84, abstract = {{GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. The implementation is done in OpenCL, and we evaluate the execution time and classification performance. We compare our approach with two other methods, FAST/BRISK and ORB. Performance data is presented for the mobile device Xperia Z3 and the desktop Nvidia GTX 660. Our results indicate that the execution times on the Xperia Z3 are insufficient for real-time applications while desktop execution shows future potential. Classification performance of Harris-Hessian/FREAK indicates that the solution is sensitive to rotation, but superior in scale variant images.}}, author = {{Danielsson, Max and Sievert, Thomas and Grahn, Håkan and Ramusson, Jim}}, booktitle = {{Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods}}, isbn = {{978-989-758-173-1}}, language = {{eng}}, pages = {{517--525}}, publisher = {{SciTePress}}, title = {{Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU}}, url = {{http://dx.doi.org/10.5220/0005662005170525}}, doi = {{10.5220/0005662005170525}}, year = {{2016}}, }