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Accelerating Fourier Descriptor for Image Recognition Using GPU |
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PP: 297-306 |
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doi:10.18576/amis/100131
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Author(s) |
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Bahri Haythem,
Sayadi Fatma,
Chouchene Marwa,
Hallek Mohamed,
Atri Mohamed,
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Abstract |
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| In the next few years, the rate of enhancement in GPUs (Graphics Processing Units) performance is expected to outshine
that of CPUs (Central Processing Units), increasing the demand of the GPU as the processor chosen for image processing. In light of
tremendous advance in computer vision research of recognition shape domain, we proposed a GPU technology of programming and
computing to accelerate the Fourier descriptor technique invariant to color images classification. It is a simple and powerful technique
to represent objects based on their shapes. It has attractive properties such as rotational, scale, and translational invariance. Since the
heaviest part of Fourier descriptor computing time is the Fast Fourier Transform (FFT), we decided to bring it out on GPU. We used
CUDA: Compute Unified Device Architecture, the specific programming language of GPU, and its CUFFT library to accelerate the
computation of FFT. To showcase this implementation, we studied the performance of GPU versus a traditional implementation on
CPU for single and double precision. |
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