Abstract
Based on hardware architecture of CUDA (Compute Unified Device Architecture), this paper not only makes full use of multithreaded and parallelism in GPU (Graphic Processing Unit, the image Processing Unit), but also takes advantage of the memory to improve parallelization of SIFT algorithm. What’s more, two-dimensional thread structure is adopted when storing the image data and variable blockIdx which is built in the device is used for mapping width and height in pixels of the two-dimensional images. Thus, it can improve the efficiency of parallel by making full use of thread parallelism and two-dimensional features of thread grid. The experimental results show that the matching accuracy and speed of the algorithm have greatly improved compared to the traditional serial SIFT algorithm, and the maximum acceleration ratio can reach 21.43 in this experiment making image parallel matching possible under the smart vehicle environment.
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Liu, HQ., Li, Yy., Li, Tt. (2015). A Parallel SIFT Algorithm for Image Matching Under Intelligent Vehicle Environment. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_12
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DOI: https://doi.org/10.1007/978-3-662-47791-5_12
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