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Proceedings Paper

Electronic image stabilization algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering
Author(s): Min Li; BingJian Wang; Xiang Yi; Jingya Hao; Feihong Wu; Hanlin Qin
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Paper Abstract

As the electronic image stabilization (EIS) algorithm based on SIFT feature matching has the problem of complex computation and time consuming, a modified EIS algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering is proposed in this paper. Firstly, feature points are extracted by using PCA-SIFT algorithm in reference frame and current frame. And the corresponding points are matched between these two images. Then the Random Sample Consensus (RANSAC) algorithm is used to eliminate the error matching pairs to reduce the influence of local motion in the scene and improve the estimation accuracy of global motion parameters. Finally, the random dithering parameters obtained by self-adaptive high-pass filtering are used to compensate the current frames. And the size of filter is adjusted automatically according to dithering frequency to prevent the overstabilization or understabilization. Experimental results show that the algorithm proposed in this paper can effectively remove vectors caused by random dithering and obtain a stable video.

Paper Details

Date Published: 24 November 2014
PDF: 7 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011H (24 November 2014); doi: 10.1117/12.2072051
Show Author Affiliations
Min Li, Xidian Univ. (China)
BingJian Wang, Xidian Univ. (China)
Xiang Yi, Xidian Univ. (China)
Jingya Hao, Xidian Univ. (China)
Feihong Wu, Xidian Univ. (China)
Hanlin Qin, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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