并行Harris特征点检测算法  

Parallel Harris Feature Point Detection Algorithm

作　　者：朱超 吴素萍 ZHU Chao;WU Su-ping(School of Information Engineering,Ningxia University,Yinchuan 750021,China)

机构地区：[1]宁夏大学信息工程学院

出　　处：《计算机科学》2019年第S11期289-293,共5页Computer Science

基　　金：国家自然科学基金项目(61662059)资助

摘　　要：针对三维重建大数据量问题中的特征点提取算法,存在运算量大、耗时多、效率低等问题,文中对Harris特征点检测算法进行改进,提出了基于OpenMP的多核CPU和基于CUDA及OpenCL框架的GPU下的Harris特征点检测并行算法。在不同实验平台进行对比实验,实验结果表明,基于CUDA及OpenCL框架的GPU并行特征点检测算法具有良好的数据和平台可扩展性,基于GPU并行特征点检测算法的加速比最高可达91.19,加速效果显著。基于OpenMP的多核CPU特征点检测算法具有良好的多核可扩展性。Harris Feature point detection is widely used in target recognition,tracking and 3D reconstruction.The computation of the feature point detection algorithm for big data problem is time-consuming and computation-intensive.There is a problem of large time-consuming and low efficiency in the algorithm of feature points detection with large data quantity.In the multi-CPU programming model based on OpenMP and GPU parallel environment based on CUDA and OpenCL architecture,In this paper,the parallel algorithm of the Harris feature point detection was proposed.In the comparison experiment of hallFeng image set on different platforms,the experimental results show that the multi-CPU feature point detection algorithm based on OpenMP shows good multi-core scalability,and the parallel feature point detection algorithms based on CUDA and OpenCL architecture in GPU parallel environment can obtain high speedup and good data and platform scalability,the maximum speed up can be more than 90 times,and the acceleration effect is significant.

关 键 词：HARRIS 特征点检测 共享存储并行编程 计算机统一设备架构 开放式计算语言 并行算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]