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Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
Sony Mobile Communications AB.
2016 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2016.
Emneord [en]
GPU, Feature Detection, Feature Description, Mobile devices
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-11444OAI: oai:DiVA.org:bth-11444DiVA, id: diva2:895562
Konferanse
5th Int’l Conf. on Pattern Recognition Applications and Methods (ICPRAM), Rome
Prosjekter
BigData@BTH - Scalable resource-efficient systems for big data analyticsIndustrial Excellence Center EASE - Embedded Applications Software Engineering
Forskningsfinansiär
Knowledge Foundation, 20140032VINNOVATilgjengelig fra: 2016-01-19 Laget: 2016-01-19 Sist oppdatert: 2018-02-02bibliografisk kontrollert

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Totalt: 564 treff
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