Fast recurrence algorithm for computing sub-Image energy using normalized cross correlation

doi:10.3788/OPE.20182610.2565

CIOMP OpenIR

	Fast recurrence algorithm for computing sub-Image energy using normalized cross correlation
	Han, Bing; Mu, Zhong-Feng; Le, Xiao-Feng; Jia, Xiao-Zhi; Shi, Xuan-Wei; Li, Bei-Bei
	2018
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	26 期号:10 页码:2565-2574
摘要	Scene matching requires higher matching speed and memory usage. In order to improve the running speed of the normalized cross correlation algorithm and reduce its memory occupancy rate,this paper focus on researching the steps of fast calculating sub-image's energy. After detailed analysis, the integral graph method has the advantages of flexible and rapid, but the defect is that it needs to spend a lot of memory at the same time, while it is not suitable for the embedded system. Therefore, a fast recurrence method was proposed. In this method, the energy of adjacent pixel values is used to continuously recursive compute. It is not necessary to allocate space for all image energy as the integral image method in the calculation process. Only one row of space can be reserved for the entire energy calculation process in fast recurrence method, which greatly saves the memory usage. The fast recurrence method has the equivalent calculation speed with the integral image method, and the time consuming is only 1/2 of the traditional normalization cross correlation algorithm. In the memory occupancy rate, the fast recurrence method is less than 1/3 of the integral image method, and the larger the size of the real-time graph, the less memory occupied by the fast delivery method. In the normalized cross correlation algorithm, the classical integral graph method and the fast recursive method proposed in this paper are used to calculate the energy of the sub-image's energy, which are both faster than the traditional NCC algorithm. The two algorithms have their advantages. The classical integration image method is fast and flexible, which is suitable for the application scene with high speed requirements, but the memory occupancy rate is not very high. The fast recursive method is fast and saves memory, and is more suitable for the application of embedded systems. 2018, Science Press. All right reserved.
关键词	Image enhancement Embedded systems Stress intensity factors
DOI	10.3788/OPE.20182610.2565
收录类别	EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/60665
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Han, Bing,Mu, Zhong-Feng,Le, Xiao-Feng,et al. Fast recurrence algorithm for computing sub-Image energy using normalized cross correlation[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2018,26(10):2565-2574.
APA	Han, Bing,Mu, Zhong-Feng,Le, Xiao-Feng,Jia, Xiao-Zhi,Shi, Xuan-Wei,&Li, Bei-Bei.(2018).Fast recurrence algorithm for computing sub-Image energy using normalized cross correlation.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,26(10),2565-2574.
MLA	Han, Bing,et al."Fast recurrence algorithm for computing sub-Image energy using normalized cross correlation".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 26.10(2018):2565-2574.

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