Authors:
Max Danielsson
1
;
Thomas Sievert
1
;
Håkan Grahn
1
and
Jim Rasmusson
2
Affiliations:
1
Blekinge Institute of Technology, Sweden
;
2
Sony Mobile Communications, Sweden
Keyword(s):
GPU, Feature Detection, Feature Description, Mobile Devices.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Pattern Recognition
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.