CLC number: TP701
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-05-05
Cited: 5
Clicked: 9246
Wen-de Dong, Yue-ting Chen, Zhi-hai Xu, Hua-jun Feng, Qi Li. Image stabilization with support vector machine[J]. Journal of Zhejiang University Science C, 2011, 12(6): 478-485.
@article{title="Image stabilization with support vector machine",
author="Wen-de Dong, Yue-ting Chen, Zhi-hai Xu, Hua-jun Feng, Qi Li",
journal="Journal of Zhejiang University Science C",
volume="12",
number="6",
pages="478-485",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000236"
}
%0 Journal Article
%T Image stabilization with support vector machine
%A Wen-de Dong
%A Yue-ting Chen
%A Zhi-hai Xu
%A Hua-jun Feng
%A Qi Li
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 6
%P 478-485
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000236
TY - JOUR
T1 - Image stabilization with support vector machine
A1 - Wen-de Dong
A1 - Yue-ting Chen
A1 - Zhi-hai Xu
A1 - Hua-jun Feng
A1 - Qi Li
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 6
SP - 478
EP - 485
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000236
Abstract: We propose an image stabilization method based on support vector machine (SVM). Since SVM is very effective in solving nonlinear regression problems, an SVM model was constructed and trained to simulate the vibration characteristic. Then this model was used to predict and compensate for the vibration. A simulation system was built and four assessment metrics including the signal-to-noise ratio (SNR), gray mean gradient (GMG), Laplacian (LAP), and modulation transfer function (MTF) were used to verify our approach. Experimental results showed that this new method allows the image plane to locate stably on the CCD, and high quality images can be obtained.
[1]Allard, J.P., Chamberland, M., Farley, V., Marcotte, F., Rolland, M., Vallières, A., Villemaire, A., 2008. Airborne measurements in the longwave infrared using an imaging hyperspectral sensor. SPIE, 7086:70860K-1-70860K-12.
[2]Ben-Ezra, M., Nayar, S.K., 2003. Motion Deblurring Using Hybrid Imaging. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1:657-664.
[3]Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Li, G.Z., Wang, M., Zeng, H.J., translators, 2004. Publishing House of Electronics Industry, Beijing, China, p.82-109 (in Chinese).
[4]Dey, N., Blanc-Feraud, L., Zimmer, C., Roux, P., Kam, Z., Olivo-Marin, J.C., Zerubia, J., 2006. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microscopy Res. Techn., 69(4):260-266.
[5]Feng, H.J., Wang, Y.P., Xu, Z.H., Li, Q., Lei, H., Zhao, J.F., 2010. Real-time deblurring algorithm with robust noise suppression. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(5):375-380.
[6]Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T., 2006. Removing camera shake from a single photograph. ACM Trans. Graph., 25(3):787-794.
[7]Fisher, M., Hadar, O., Kopeika, N.S., 1990. Numerical calculation of modulation transfer functions for low-frequency mechanical vibrations. SPIE, 1342:72-83.
[8]Goodman, J.W., 2004. Introduction to Fourier Optics (3rd Ed.). Robert & Company Publisher, USA, p.127-173.
[9]ISO 12233:2000. Photography-Electronic Still-Picture Cameras-Resolution Measurements. International Organization for Standardization, Geneva.
[10]Janschek, K., Tchernykh, V., 2001. Optical correlator for image motion compensation in the focal plane of a satellite camera. Space Technol., 21(4):127-132.
[11]Kusiak, A., Zheng, H., Song, Z., 2009. Short-term prediction of wind farm power: a data mining approach. IEEE Trans. Energy Conv., 24(1):125-136.
[12]Lewis, J.P., 1995. Fast Template Matching. Vision Interface, Canadian Image Processing and Pattern Recognition Society, p.120-123.
[13]Sapankevych, N., Sankar, R., 2009. Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag., 4(2):24-38.
[14]Shan, Q., Jia, J., Agarwala, A., 2008. High-quality motion deblurring from a single image. ACM Trans. Graph., 27(3).
[15]Shi, Z.W., Han, M., 2007. Support vector echo-state machine for chaotic time-series prediction. IEEE Trans. Neur. Networks, 18(2):359-372.
[16]Yuan, L., Sun, J., Quan, L., Shum, H.Y., 2008. Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph., 27(3).
[17]Zhang, W., Zhao, M., Wang, Z., 2009. Adaptive wavelet-based deconvolution method for remote sensing imaging. Appl. Opt., 48(24):4785-4793.
[18]Zhao, J.H., Zhao, Y.D., Zhao, X., Wong, K.P., 2008. A statistical approach for interval forecasting of the electricity price. IEEE Trans. Power Syst., 23(2):267-276.
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