Electronic image stabilization algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering

Min Li; BingJian Wang; Xiang Yi; Jingya Hao; Feihong Wu; Hanlin Qin

doi:10.1117/12.2072051

24 November 2014 Electronic image stabilization algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering

Min Li, BingJian Wang, Xiang Yi, Jingya Hao, Feihong Wu, Hanlin Qin

Author Affiliations +

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93011H (2014) https://doi.org/10.1117/12.2072051
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

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.

Citation Download Citation

Min Li, BingJian Wang, Xiang Yi, Jingya Hao, Feihong Wu, and Hanlin Qin "Electronic image stabilization algorithm based on PCA-SIFT feature matching and self-adaptive high-pass filtering", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011H (24 November 2014); https://doi.org/10.1117/12.2072051

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KEYWORDS

Linear filtering

Optical filters

Cameras

Image filtering

Detection and tracking algorithms

Image processing

Motion estimation

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