关键点检测的快速高斯SIFT及其硬件结构

L. Ke, J. Wang, Xi-Juan Zhao, Fan Liang
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引用次数: 3

摘要

尺度不变特征变换(SIFT)是一种从图像中提取具有显著性和不变性的特征,从而在不同尺度和旋转下实现不同图像之间可靠目标匹配的算法。然而,SIFT庞大的计算量阻碍了其实时性的实现。本文提出了一种快速高斯滤波算法(FG-SIFT)。重点检测在FG-SIFT中进行了优化。SIFT在高斯尺度空间(GSS)中的二维高斯差值(DoG)在x和y两个维度上被分割成两个一维DoG, DoG金字塔中的尺度水平也被降低。实验表明,FG-SIFT算法在构建GSS时将计算复杂度降低了95%左右,同时提高了关键点检测的精度。随后,生成的特征精度提高了162%,匹配的特征精度提高了8%。在算法层面进行优化的基础上,提出了FG-SIFT关键点检测模块的硬件结构。使用包含五阶段流水线的并行架构,关键点检测的执行时间仅为1.42ms@Xilinx Virtex5。与传统作品相比,速度比最快的解决方案[9](ASIC)快21%,硬件资源比最节省资源的解决方案[5][6](Xilinx Virtex5)少70%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast-Gaussian SIFT and its hardware architecture for keypoint detection
Scale invariant feature transform(SIFT) is an algorithm to extract distinctive and invariant features from images to achieve reliable object matching between different images in variant scales and rotations. However, SIFT's huge computation impedes its real-time implementation. In this paper, a Fast-Gaussian SIFT(FG-SIFT) is proposed. Keypoint detection is optimized in FG-SIFT. SIFT's 2-D difference of Gaussian(DoG) in Gaussian Scale-Space(GSS) is separated into two 1-D DoG in x and y dimensions, and the level of scales in DoG pyramid is also reduced. The experiment shows that FG-SIFT reduces the computational complexity about 95% in GSS construction, also increases the accuracy of keypoint detection. Subsequently, the accuracy of generated features is increased 162%, and the accuracy of matched features is increased 8%. Apart from optimization in algorithm level, a hardware architecture of FG-SIFT's keypoint detection module is proposed. With a parallel architectural incorporating a five-stage pipeline, the execution time of keypoint detection is only 1.42ms@Xilinx Virtex5. Compared to conventional works, the speed is 21% faster than the fastest solution [9](ASIC), and hardware resources are 70% less than the most resources saved solutions [5] [6](Xilinx Virtex5).
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