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Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Sony Mobile Communications AB.
2016 (English)Conference paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2016.
Keyword [en]
GPU, Feature Detection, Feature Description, Mobile devices
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:bth-11444OAI: oai:DiVA.org:bth-11444DiVA: diva2:895562
Conference
5th Int’l Conf. on Pattern Recognition Applications and Methods (ICPRAM), Rome
Projects
BigData@BTH - Scalable resource-efficient systems for big data analyticsIndustrial Excellence Center EASE - Embedded Applications Software Engineering
Funder
Knowledge Foundation, 20140032VINNOVA
Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2017-03-13Bibliographically approved

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Sievert, ThomasGrahn, Håkan
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf