Fast Canny algorithm based on GPU+CPU

Bin TANG; Wen LONG

doi:10.3788/YJYXS20163107.0714

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Fast Canny algorithm based on GPU+CPU

Display and Image | 更新时间：2020-07-07

- Fast Canny algorithm based on GPU+CPU
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 7, Pages: 714-720(2016)
- 作者机构：
  
  1.贵州财经大学信息学院, 贵州贵阳 550025
  2.贵州财经大学贵州省经济系统仿真重点实验室, 贵州贵阳 550025
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20163107.0714
  CLC： TP333
- Received：21 December 2015，
  
  Accepted：03 March 2016，
  
  Published：2016-07
- 稿件说明：
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Bin TANG, Wen LONG. Fast Canny algorithm based on GPU+CPU[J]. Chinese journal of liquid crystals and displays, 2016, 31(7): 714-720.
DOI：

Bin TANG, Wen LONG. Fast Canny algorithm based on GPU+CPU[J]. Chinese journal of liquid crystals and displays, 2016, 31(7): 714-720. DOI： 10.3788/YJYXS20163107.0714.

摘要

本文提出一种基于GPU+CPU的快速实现Canny算子的方法。首先将算子分为串行和并行两部分，高斯滤波、梯度幅值和方向计算、非极大值抑制和双阈值处理在GPU中完成，将二维高斯滤波分解为水平方向上和垂直方向上的两次一维滤波从而降低计算的复杂度；然后使用CUDA编程完成多线程并行计算以加快计算速度；最后使用共享存储器隐藏线程访问全局存储的延迟；在CPU中则使用队列FIFO完成边缘连接。仿真测试结果表明：对分辨率为1024×1024的8位图像的处理时间为122 ms，相对应单独使用CPU而言，加速比最高可达5.39倍，因此本文方法充分利用了GPU的并行性的特征和CPU的串行处理能力。

Abstract

This paper presents a fast method for Canny algorithm based on GPU+CPU. The Canny algorithm is divided into two parts:Gauss filtering

gradient computations

non maximum suppression and double thresholding are processed by GPU. The fast method convert two-dimensional Gaussian filter to two separable convolutions to reduce the computation complexity. Then

multiple threads execute kernel in parallel to speed up the computation in the CUDA program. Finally

threads access shared memory instead of global memory to hide the latencies of global memory. In addition

FIFO is used to connect components in CPU. The simulation results show that the processing time of the 8-bit images with the resolution 1 024×1 024 is 122 ms

which is 5.39 times faster than CPU. Therefore

this method takes full advantage of the parallelism of GPU and the serial processing capability of CPU.

关键词

Keywords

references

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