Paper Info
Title
Efficient parallel optimizations of a high-performance SIFT on GPUs.
Abstract
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∼7.71 ms, which is 55.88∼121.99 times, 5.17∼6.88 times, and 1.25∼1.79 times faster than OpenCV SIFT, SiftGPU, and CudaSift, respectively.
Year
DOI
Venue
2019
10.1016/j.jpdc.2018.10.012
Journal of Parallel and Distributed Computing
Keywords
Field
DocType
HartSift,SIFT,GPU,High performance,Feature extraction
Computer vision,Scale-invariant feature transform,Data processing,Image stitching,Feature extraction algorithm,Feature detection,Computer science,Parallel computing,Artificial intelligence,Image registration,Computational complexity theory
Journal
Volume
ISSN
Citations 
124
0743-7315
1
PageRank 
References 
Authors
0.40
26
7
Name
Order
Citations
PageRank
Zhihao Li1175.10
Haipeng Jia2222.20
Yunquan Zhang332743.92
Shice Liu4131.72
Shigang Li528243.13
Xiao Wang642.83
Hao Zhang710.40