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Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. In this paper, we propose an robust and efficient video stabilization algorithm based on inter-frame image matching score. Firstly, image matching is performed by a method combining Maximally Stable Extremal Regions (MSERs) detection algorithm and Features from Accelerated Segment Test (FAST) corner detection algorithm, which can get the matching score and the motion parameters of the frame image. Then, the matching score is filtered to filter out the high frequency component and keep the low frequency component. And the motion compensation is performed on the current frame image according to the ratio of the matching score before and after the filtering to retain the global motion and remove the local jitter. Various classical corner detection operators and region matching operators are compared in experiments.
Qinghe Zheng. 2017. \u201cA Video Stabilization Method Based on Inter-frame Image Matching Scorea\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 17 (GJCST Volume 17 Issue F1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 132
Country: China
Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision
Authors: Qinghe Zheng, Mingqiang Yang (PhD/Dr. count: 0)
View Count (all-time): 269
Total Views (Real + Logic): 6951
Total Downloads (simulated): 1751
Publish Date: 2017 04, Tue
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Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. In this paper, we propose an robust and efficient video stabilization algorithm based on inter-frame image matching score. Firstly, image matching is performed by a method combining Maximally Stable Extremal Regions (MSERs) detection algorithm and Features from Accelerated Segment Test (FAST) corner detection algorithm, which can get the matching score and the motion parameters of the frame image. Then, the matching score is filtered to filter out the high frequency component and keep the low frequency component. And the motion compensation is performed on the current frame image according to the ratio of the matching score before and after the filtering to retain the global motion and remove the local jitter. Various classical corner detection operators and region matching operators are compared in experiments.
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