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Efficient parallel optimizations of a high-performance SIFT on GPUs

文献类型：期刊论文

作者Liu, Shice2,3; Li, Zhihao2,3; Jia, Haipeng3; Zhang, Yunquan3; Li, Shigang3; Wang, Xiao2,3; Zhang, Hao1
刊名JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
出版日期2019-02-01
卷号124页码:78-91
关键词HartSift SIFT CPU High performance Feature extraction
ISSN号0743-7315
DOI10.1016/j.jpdc.2018.10.012
英文摘要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.
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
语种英语
WOS记录号WOS:000452939400008
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/3508]  
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.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shice,Li, Zhihao,Jia, Haipeng,et al. Efficient parallel optimizations of a high-performance SIFT on GPUs[J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING,2019,124:78-91.
APA Liu, Shice.,Li, Zhihao.,Jia, Haipeng.,Zhang, Yunquan.,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 Liu, Shice,et al."Efficient parallel optimizations of a high-performance SIFT on GPUs".JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 124(2019):78-91.

入库方式： OAI收割

来源：计算技术研究所

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