ISSN 0253-2778

CN 34-1054/N

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Parallel implementation of surf algorithm based on OpenCL

  • School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.10.002
  • Received Date: 01 April 2017
  • Rev Recd Date: 05 August 2017
  • Publish Date: 31 October 2017
    Abstract

  • Abstract

    SURF algorithm has high computational complexity and can not meet the real-time requirement. To solve these problems, a parallel SURF algorithm based on OpenCL was presented. Firstly, data parallelism and task parallelism model were applied to the calculations of the integral images, Hessian detector, orientation and descriptor, and the detailed algorithm process was given. Secondly, the performance of the parallel algorithm was optimized from data transmission, memory access and load balancing. The experimental results show that the algorithm can achieve more than 10 times speedup for images with different resolution, and some high-resolution images can even reach up to 39.5 times. Furthermore, it can be applied to a variety of general purpose computing platforms.

    Abstract

    SURF algorithm has high computational complexity and can not meet the real-time requirement. To solve these problems, a parallel SURF algorithm based on OpenCL was presented. Firstly, data parallelism and task parallelism model were applied to the calculations of the integral images, Hessian detector, orientation and descriptor, and the detailed algorithm process was given. Secondly, the performance of the parallel algorithm was optimized from data transmission, memory access and load balancing. The experimental results show that the algorithm can achieve more than 10 times speedup for images with different resolution, and some high-resolution images can even reach up to 39.5 times. Furthermore, it can be applied to a variety of general purpose computing platforms.
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    • References(11)
  • [1]
    赵向阳, 杜利民. 一种全自动稳健的图像拼接融合算法[J]. 中国图象图形学报: A 辑, 2004, 9(4): 417-422.
    ZHAO Xiangyang, DU Limin. An automatic robust mosaic algorithm[J]. Journal of Image and Graphics, 2004, 9(4): 417-422.
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    WANG Y T, FENG Y C. Data association and map management for robot SLAM using local invariant features[C]// International Conference on Mechatronics and Automation. Takamatsu, Japan: IEEE, 2013: 1102-1107.
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    BAY H, TUYTELAARS T, VAN GOOL L. Surf: Speeded up robust features[J]. Computer Vision & Image Understanding, 2006, 110(3): 404-417.
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    LEUTENEGGER S, CHLI M, SIEGWART R Y. BRISK: Binary robust invariant scalable key points[C]// Proceedings of the International Conference on Computer Vision. Washington: IEEE, 2011: 2548-2555.
    [5]
    赵春阳, 赵怀慈. SURF 算法并行优化及硬件实现[J]. 计算机辅助设计与图形学学报, 2015, 27(2): 256-263.
    ZHAO Chunyang, ZHAO Huaici. Parallel optimized method and hardware implementation of SURF algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(2): 256-263.
    [6]
    SVAB J, KRAJNIK T, FAIGL J, et al. FPGA based speeded up robust features[C]// Proceedings of the International Conference on Technologies for Practical Robot Applications. Woburn, USA: IEEE, 2009: 35-41.
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    刘金硕, 曾秋梅, 邹斌, 等. 快速鲁棒特征算法的 CUDA 加速优化[J]. 计算机科学, 2014, 41(4): 24-27.
    LIU Jinshuo, ZENG Qiumei, ZOU Bin, et al. Speed-up robust feature image registration algorithm based on CUDA[J]. Computer Science, 2014, 41(4): 24-27.
    [8]
    徐晶, 曾苗祥, 许炜. 基于 GPU 的图片特征提取与检测[J]. 计算机科学, 2014, 41(7): 157-161.
    XU Jing, ZENG Miaoxiang, XU Wei. GPU based image feature extraction and detection[J]. Computer Science, 2014, 41(7): 157-161.
    [9]
    SINHA S N, FRAHM J M, POLLEFEYS M, et al. GPU-based video feature tracking and matching[R]. MSR-TR-2016-19, Workshop on Edge Computing Using New Commodity Architectures, 2006.
    [10]
    CAO J, XIE X F, LIANG J, et al. GPU accelerated target tracking method[J]. Advances in Multimedia, Software Engineering and Computing, 2012, 1: 251-257.
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    Khronos OpenCL Working Group. The OpenCL Specification Version1.2[EB/OL]. [2017-10-18]https://www.khronos.org/registry/OpenCL/specs/opencl-1.2.pdf.
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    [1]
    赵向阳, 杜利民. 一种全自动稳健的图像拼接融合算法[J]. 中国图象图形学报: A 辑, 2004, 9(4): 417-422.
    ZHAO Xiangyang, DU Limin. An automatic robust mosaic algorithm[J]. Journal of Image and Graphics, 2004, 9(4): 417-422.
    [2]
    WANG Y T, FENG Y C. Data association and map management for robot SLAM using local invariant features[C]// International Conference on Mechatronics and Automation. Takamatsu, Japan: IEEE, 2013: 1102-1107.
    [3]
    BAY H, TUYTELAARS T, VAN GOOL L. Surf: Speeded up robust features[J]. Computer Vision & Image Understanding, 2006, 110(3): 404-417.
    [4]
    LEUTENEGGER S, CHLI M, SIEGWART R Y. BRISK: Binary robust invariant scalable key points[C]// Proceedings of the International Conference on Computer Vision. Washington: IEEE, 2011: 2548-2555.
    [5]
    赵春阳, 赵怀慈. SURF 算法并行优化及硬件实现[J]. 计算机辅助设计与图形学学报, 2015, 27(2): 256-263.
    ZHAO Chunyang, ZHAO Huaici. Parallel optimized method and hardware implementation of SURF algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2015, 27(2): 256-263.
    [6]
    SVAB J, KRAJNIK T, FAIGL J, et al. FPGA based speeded up robust features[C]// Proceedings of the International Conference on Technologies for Practical Robot Applications. Woburn, USA: IEEE, 2009: 35-41.
    [7]
    刘金硕, 曾秋梅, 邹斌, 等. 快速鲁棒特征算法的 CUDA 加速优化[J]. 计算机科学, 2014, 41(4): 24-27.
    LIU Jinshuo, ZENG Qiumei, ZOU Bin, et al. Speed-up robust feature image registration algorithm based on CUDA[J]. Computer Science, 2014, 41(4): 24-27.
    [8]
    徐晶, 曾苗祥, 许炜. 基于 GPU 的图片特征提取与检测[J]. 计算机科学, 2014, 41(7): 157-161.
    XU Jing, ZENG Miaoxiang, XU Wei. GPU based image feature extraction and detection[J]. Computer Science, 2014, 41(7): 157-161.
    [9]
    SINHA S N, FRAHM J M, POLLEFEYS M, et al. GPU-based video feature tracking and matching[R]. MSR-TR-2016-19, Workshop on Edge Computing Using New Commodity Architectures, 2006.
    [10]
    CAO J, XIE X F, LIANG J, et al. GPU accelerated target tracking method[J]. Advances in Multimedia, Software Engineering and Computing, 2012, 1: 251-257.
    [11]
    Khronos OpenCL Working Group. The OpenCL Specification Version1.2[EB/OL]. [2017-10-18]https://www.khronos.org/registry/OpenCL/specs/opencl-1.2.pdf.
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