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Efficient parallel Recursive Gaussian SIFT algorithm based on multi-core DSP

机译:基于多核DSP的高效并行递归高斯SIFT算法

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To solve the problem of high computational complexity and real-time poor of the SIFT(Scale Invariant Feature Transform) algorithm, a parallel data streams RGF-SIFT(Recursive Gaussian Filter-SIFT) algorithm based on DSP multi-core processor is proposed. The proposed algorithm uses the forth-order recursive Gaussian filter to replace the linear Gaussian filtering of the SIFT algorithm. Then the four modules of RGF-SIFT computing tasks are assigned to multiple DSP core for parallel processing, and implemented synchronization for multicore processor through inter-processor communication (IPC) and other technologies. Experimental results show that the parallel RGF-SIFT algorithm detects feature point more than the algorithm of SIFT, and the repetition rate of the correct feature point is very high. At execution time, the parallel RGF-SIFT algorithm has higher speedup ratio.
机译:为了解决SIFT算法的高计算复杂度和实时性差的问题,提出了一种基于DSP多核处理器的并行数据流RGF-SIFT(递归高斯滤波器-SIFT)算法。所提出的算法使用四阶递归高斯滤波器来代替SIFT算法的线性高斯滤波器。然后将RGF-SIFT计算任务的四个模块分配给多个DSP内核进行并行处理,并通过处理器间通信(IPC)和其他技术为多核处理器实现同步。实验结果表明,并行RGF-SIFT算法比SIFT算法对特征点的检测要多,正确特征点的重复率非常高。在执行时,并行RGF-SIFT算法具有更高的加速比。

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