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Viability of Feature Detection on Sony Xperia Z3 using OpenCL
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies. Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies. Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on load distribution on the device.

Objectives. We evaluate the viability of a feature detector and de-scriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance.

Methods. We implement the feature detection and feature descrip-tor pair Harris-Hessian/FREAK for GPU execution using OpenCL,focusing on embedded platforms. We then study the heat generationof the application, its execution time and compare our method to twoother methods, FAST/BRISK and ORB, to evaluate the vision per-formance.

Results. Execution time data for the Xperia Z3 and desktop GeForceGTX660 is presented. Run time temperature values for a run ofnearly an hour are presented with correlating CPU and GPU ac-tivity. Images containing comparison data for BRISK, ORB andHarris-Hessian/FREAK is shown with performance data and discus-sion around notable aspects.

Conclusion. Execution times on Xperia Z3 is deemed insufficientfor real-time applications while desktop execution shows that there isfuture potential. Heat generation is not a problem for the implemen-tation. Implementation improvements are discussed to great lengthfor future work. Performance comparisons of Harris-Hessian/FREAKsuggest that the solution is very vulnerable to rotation, but superiorin scale variant images. Generally appears suitable for near duplicatecomparisons, delivering much greater number of keypoints. Finally,insight to OpenCL application development on Android is given

Place, publisher, year, edition, pages
2015. , 58 p.
Keyword [en]
GPU, Feature Detection, Feature Description, Embedded Device
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-10388OAI: oai:DiVA.org:bth-10388DiVA: diva2:839503
External cooperation
SONY Mobile
Subject / course
DV2524 Degree Project in Computer Science for Engineers
Educational program
PAACI Master of Science in Game and Software Engineering
Supervisors
Examiners
Available from: 2015-08-04 Created: 2015-07-02 Last updated: 2015-08-04Bibliographically approved

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CiteExportLink to record
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