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http://dx.doi.org/10.5909/JBE.2014.19.3.316

Parallelization of Feature Detection and Panorama Image Generation using OpenCL and Embedded GPU  

Kang, Seung Heon (Department of Information and Communication)
Lee, Seung-Jae (Department of Information and Communication)
Lee, Man Hee (Department of Information and Communication)
Park, In Kyu (Department of Information and Communication)
Publication Information
Journal of Broadcast Engineering / v.19, no.3, 2014 , pp. 316-328 More about this Journal
Abstract
In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and its application to fast panoramic image generation on the latest embedded GPU. Parallelized algorithms are implemented using recently developed OpenCL as the embedded GPGPU software platform. We compare the implementation efficiency and speed performance of conventional OpenGL Shading Language and OpenCL. Experimental result shows that implementation on OpenCL has comparable performance with GLSL. Compared with the performance on the embedded CPU in the same application processor, the embedded GPU runs 3~4 times faster. As an example of using feature extraction, panorama image synthesis is performed on embedded GPU by applying image matching using detected features.
Keywords
Embedded GPGPU; OpenCL; OpenGL; SIFT; SURF; panorama image;
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Times Cited By KSCI : 1  (Citation Analysis)
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