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Fast Implementation of Scale Invariant Feature Transform Based on CUDA |
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PP: 717-722 |
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Author(s) |
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Meng Lu,
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Abstract |
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| Scale-invariant feature transform (SIFT) was an algorithm in computer vision to detect and describe local features in images.
Due to its excellent performance, SIFT was widely used in many applications, but the implementation of SIFT was complicated and
time-consuming. To solve this problem, this paper presented a novel acceleration algorithm for SIFT implementation based on Compute
Unified Device Architecture (CUDA). In the algorithm, all the steps of SIFT were specifically distributed and implemented by CPU
or GPU, accroding to the step’s characteristics or demandings, to make full use of computational resources. Experiments showed that
compared with the traditional implementation of SIFT, this paper’s acceleration algorithm can greatly increase computation speed and
save implementation time. Furthermore, the acceleration ratio had linear relation with the number of SIFT keypoints. |
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