基于GPU的SIFT特征匹配算法并行处理研究  

Parallel Processing Research on SIFT Feature Matching Algorithm Based on GPU

作　　者：姜超[1] 耿则勋[1] 娄博[1,2] 魏小峰[1] 沈忱[1] 

机构地区：[1]解放军信息工程大学 [2]69028部队

出　　处：《计算机科学》2013年第12期295-297,307,共4页Computer Science

基　　金：863项目(2012AA7032031D)资助

摘　　要：SIFT算法因具有旋转、缩放以及平移不变性而在影像配准和基于影像的三维重建领域得到广泛应用。但该算法复杂度较高,在CPU上执行的效率不高,难以满足对实时性要求较高的应用。在深入分析SIFT算法原理的基础上,针对该算法提取特征的多量性和特征向量的高维性,将该算法进行了并行化改造以利用GPU强大的并行计算能力,并与CPU上实现的SIFT算法进行了比较。实验证明,基于GPU的SIFT算法执行效率大幅提升,平均可以达到10倍以上的加速比。SIFT algorithm has invariance of rotation, scale and translation, so it is used widely in the field of image matching and 3D reconstruction. But the SIFT algorithm is complicate, making the processing speed slow, difficult to meet the application of high real-time requirements. On the basis of analysis of the principle of SIFT algorithm, in view of the large numbers of extracted features,high-dimensional of the feature vector, we refined the algorithm for parallel processing to take advantage of modem graphics hardware, and compared it with the CPU SIFT algorithms. Experi- ments demonstrate that the algorithm based on GPU SIFT significantly increases efficiency, and can reach the speed ra- tio of more than ten times averagely.

关 键 词：GPU SIFT CUDA 特征匹配 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]