Canny算法的GPU并行加速  

Parallel acceleration of Canny algorithm based on GPU

作　　者：张帆[1,2] 韩树奎 张立国 王文胜[1,2] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]中国科学院大学,北京100049 [3]东北电力设计研究院,吉林长春130021

出　　处：《中国光学》2017年第6期737-743,共7页Chinese Optics

基　　金：国家高技术研究发展计划(863计划)资助项目(No.863-2-5-1-13B)~~

摘　　要：Canny算法在PC机上的执行速度较慢,这极大地限制了其实用性。本文在前人的研究基础上对算法进行更深的优化和改进。首先在VS2012开发环境下利用数字图像处理技术对原算法进行原理上的改进,再利用GPU流处理器数量众多的优势以及强大的多线程并发执行能力对Canny算法进行并行加速。在500 pixel×500 pixel的图片上,对本文算法和原Canny算法进行了实验验证。实验结果表明,在4 096 pixel×4 096 pixel大小的图片上采用本文的GPU移植算法处理后,执行速度从80 ms降到了6 ms以内。在不影响边缘检测效果的前提下极大地提高了算法的实用性。Due to the slow execution speed of Canny algorithm in PC,the practicality of this algorithm is greatly restricted.Based on the previous studies,we further optimizes and improves the algorithm.First of all,we use the digital image processing technology to improve the original algorithm under the development environment of VS2012,and then accelerate the Canny algorithm by taking advantage of the large number of GPU stream processors and powerful multithreaded concurrent execution capability.Experiments were made on the improved algorithm and the original Canny algorithm.Experimental results show that in the 4 096×4 096 pixelsize images,the GPU migration algorithm presented in this paper can reduce the execution speed from 80 ms to less than 6 ms.Through this improvement,it can greatly improve the practicability of the algorithm without affecting the edge detection effect.

关 键 词：边缘检测 GPU 并行处理 连通域提取 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]