东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (2): 200-204.DOI: -

• 信息与控制 • 上一篇    下一篇

基于CUDA加速的SIFT特征提取

王蓓蕾1,朱志良1,孟琭2   

  1. (1.东北大学软件学院,辽宁沈阳110819;2.东北大学信息科学与工程学院,辽宁沈阳110819)
  • 收稿日期:2012-08-21 修回日期:2012-08-21 出版日期:2013-02-15 发布日期:2013-04-04
  • 通讯作者: 王蓓蕾
  • 作者简介:王蓓蕾(1979-),女,辽宁沈阳人,东北大学博士研究生,讲师;朱志良(1962-),男,辽宁沈阳人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61101057).

CUDAbased Acceleration Algorithm of SIFT Feature Extraction

WANG Beilei1, ZHU Zhiliang1, MENG Lu2   

  1. 1. School of Software, Northeastern University, Shenyang 110819, China; 2. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2012-08-21 Revised:2012-08-21 Online:2013-02-15 Published:2013-04-04
  • Contact: WANG Beilei
  • About author:-
  • Supported by:
    -

摘要: 提出一种基于统一计算设备架构(CUDA)加速的尺度不变特征变换(SIFT)快速计算方法,用以解决SIFT特征提取计算过程耗时过长的问题.该方法充分利用图像处理单元(GPU)在并行计算、浮点计算、内存管理等方面的优势,合理分配主机端和设备端的资源及其在SIFT特征计算中所承担的角色.实验表明,与CPU架构下的SIFT特征提取算法相比,本文算法可以大幅度加快SIFT特征提取的计算速度,其加速比随着SIFT特征点数目的增加而增加,在本文实验中最大加速比可达1954.

关键词: CUDA加速, 尺度不变特征变换, 图像特征, 特征描述符, 图像处理单元

Abstract: A novel algorithm for accelerating scaleinvariant feature transform (SIFT) was presented on the basis of compute unified device architecture (CUDA), which could solve the timeconsuming problem in SIFT feature extracting. This algorithm took the advantages of graphic processing unit (GPU) in parallel computation, float point computation and memory management, and reasonably allocated the computational resources and the corresponding computational tasks to the host and device in the SIFT feature extracting. Experimental results show that, compared with the CPUbased acceleration algorithm, the proposed CUDAbased algorithm greatly speeds up the extracting of SIFT features. The acceleration ratio increases with the number of SIFT feature points. The maximum acceleration ratio in the experiments was 1954.

Key words: CUDA acceleration, scaleinvariant feature transform, image feature, feature descriptor, GPU

中图分类号: