Abstract
Speeded-Up Robust Features (SURF), an image local feature extracting and describing method, finds and describes point correspondences between images with different viewing conditions. Despite the fact that it has recently been developed, SURF has already successfully found its applications in the area of computer vision, and was reported to be more appealing than the earlier Scale-Invariant Feature Transform (SIFT) in terms of robustness and performance. This paper presents a multi-threaded algorithm and its implementation that computes the same SURF. The algorithm parallelises several stages of computations in the original, sequential design. The main benefit brought about is the acceleration in computing the descriptor. Tests have been performed to show that the parallel SURF (P-SURF) generally shortened the computation time by a factor of 2 to 6 than the original, sequential method when running on multi-core processors.
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Zhang, N. (2009). Computing Parallel Speeded-Up Robust Features (P-SURF) via POSIX Threads. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_33
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DOI: https://doi.org/10.1007/978-3-642-04070-2_33
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