Efficient parallel optimizations of a high-performance SIFT on GPUs

doi:10.1016/j.jpdc.2018.10.012

	Efficient parallel optimizations of a high-performance SIFT on GPUs
	Li, Zhihao 1,2; Jia, Haipeng 1; Zhang, Yunquan 1; Liu, Shice 1,2; Li, Shigang 1; Wang, Xiao 1,2; Zhang, Hao 3
	2019-02-01
发表期刊	JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
ISSN	0743-7315
卷号	124 页码:78-91
摘要	Stable local image feature detection is a fundamental problem in computer vision and is critical for obtaining the corresponding interest points among images. As a popular and robust feature extraction algorithm, the scale invariant feature transform (SIFT) is widely used in various domains, such as image stitching and remote sensing image registration. However, the computational complexity of SIFT is extremely high, which limits its application in real-time systems and large-scale data processing tasks. Thus, we propose several efficient optimizations to realize a high-performance SIFT (HartSift) by exploiting the computing resources of CPUs and GPUs in a heterogeneous machine. Our experimental results show that HartSift processes an image within 3.07 similar to 7.71 ms, which is 55.88 similar to 121.99 times, 5.17 similar to 6.88 times, and 1.25 similar to 1.79 times faster than OpenCV SIFT, SiftGPU, and CudaSift, respectively. (C) 2018 Elsevier Inc. All rights reserved.
关键词	HartSift SIFT CPU High performance Feature extraction
DOI	10.1016/j.jpdc.2018.10.012
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61602443] ; National Natural Science Foundation of China[61432018] ; National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[61502450] ; National Key Research and Development Program of China[2107YFB0202105] ; National Key Research and Development Program of China[2016YFE0100300] ; National Key Research and Development Program of China[2017YFB0202302] ; Key Technology Research and Development Programs of Guangdong Province[2015B010108006]
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:000452939400008
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3509
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jia, Haipeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China 3.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhihao,Jia, Haipeng,Zhang, Yunquan,et al. Efficient parallel optimizations of a high-performance SIFT on GPUs[J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING,2019,124:78-91.
APA	Li, Zhihao.,Jia, Haipeng.,Zhang, Yunquan.,Liu, Shice.,Li, Shigang.,...&Zhang, Hao.(2019).Efficient parallel optimizations of a high-performance SIFT on GPUs.JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING,124,78-91.
MLA	Li, Zhihao,et al."Efficient parallel optimizations of a high-performance SIFT on GPUs".JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 124(2019):78-91.