Abstract
In recent years, a digital video stabilization improving the results of hand-held shooting or shooting from mobile platforms is the most popular approach. In this chapter, the task of digital video stabilization in static scenes is investigated. The unwanted motion caused by camera jitters or vibrations ought to be separated from the objects motion in a scene. Our contribution connects with the development of deblurring method to find and improve the blurred frames, which have strong negative influence on the following processing results. The use of fuzzy Takagi-Sugeno-Kang model for detection the best local and global motion vectors is the novelty of our approach. The quality of test videos stabilization was estimated by Peak Signal to Noise Ratio (PSNR) and Interframe Transformation Fidelity (ITF) metrics. Experimental data confirmed that the ITF average estimations increase up on 3–4 dB or 15–20 % relative to the original video sequences.
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References
Marcenaro, L., Vernazza, G., Regazzoni, C.S.: Image stabilization algorithms for video surveillance applications. Int. Conf. on Image Process. 1, 349–352 (2001). Thessaloniki, Greece
Peng, Y.C., Liang, C.K., Chang, H.A., Chen, H.H., Kao, C.J.: Integration of image stabilizer with video codec for digital video cameras. In: International Symposium on Circuits and Systems, pp. 4781–4784. Kobe (2005)
Rawat, P., Singhai, J.: Review of motion estimation and video stabilization techniques for hand held mobile video. Int. J. Sig. Image Process. 2(2), 159–168 (2011)
Tanakian, M.J., Rezaei, M., Mohanna, F.: Digital video stabilization system by adaptive motion vector validation and filtering. In: International Conference on Communications Engineering, pp. 165–183. Zahedan (2010)
Tanakian, M.J., Rezaei, M., Mohanna, F.: Digital video stabilization system by adaptive fuzzy filtering. In: Proceedings of the 19th European Signal Processing Conference, pp. 318–322. Barcelona (2011)
Battiato, S., Gallo, G., Puglisi, G., Scellato, S.: Fuzzy-based motion estimation for video stabilization using SIFT interest points. In: SPIE Electronic Imaging 2009—System Analysis for Digital Photography V EI-7250, pp. 1–8 (2009)
Acharjee, S., Chaudhuri, S.S.: Fuzzy logic based three step search algorithm for motion vector estimation. Int. J. Image Graph. Sig. Process. 2, 37–43 (2012)
Gullu, M.K., Erturk, S.: Membership function adaptive fuzzy filter for image sequence stabilization. IEEE Trans. Consum. Electron. 50(1), 1–7 (2004)
Sugeno, M.: Industrial applications of fuzzy control. Elsevier Science Inc., New York (1985)
Kyriakoulis, N., Gasteratos, A.A.: Recursive fuzzy system for efficient digital image stabilization. Advan. in Fuzzy Syst. 2008, 1–8 (2008)
Shen, Y., Guturu, P., Damarla, T., Buckles, B.P., Namuduri, K.R.: Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework. IEEE Trans. on Consum. Electron. 55(3), 1714–1721 (2009)
Tsai, D., Lai, S.: Defect detection in periodically patterned surfaces using independent component analysis. Pattern Recogn. 41(9), 2812–2832 (2008)
Kim, N., Lee, H., Lee, J.: Probabilistic global motion estimation based on Laplacian two-bit plane matching for fast digital image stabilization. EURASIP J. Adv. Sig. Process. pp. 1–10 (2008)
Pun, C.M., Lee, M.C.: Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 590–603 (2003)
Shakoor, M.H., Moattari, M.: Statistical digital image stabilization. J. Eng. Technol. Res. 3(5), 161–167 (2011)
Cho, S., Wang, J.: Video deblurring for hand-held cameras using patch-based synthesis. ACM Trans. Graph. (SIGGRAPH 2012) 31(4), 1–64 (2012)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28(5), 1–8 (2009)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 1–10 (2008)
Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. Int. J. Comput. Vis. 98(2), 168–186 (2012)
Gupta, A., Joshi, N., Zitnick, C.L., Cohen, M., Curless, B.: Single image deblurring using motion density functions. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision—ECCV 2010, LNCS 6311, Part 1, pp. 171–184. Springer, Heidelberg (2010)
Cai, J., Walker, R.: Robust video stabilization algorithm using feature point selection and delta optical flow. IET Comput. Vis. 3(4), 176–188 (2009)
Cho, S., Matsushita, Y., Lee, S.: Removing non-uniform motion blur from images. In: 11th International Conference on Computer Vision, pp. 1–8. Rio de Janeiro (2007)
Matsushita, Y., Ofek, E., Ge, W., Tang, X., Shum, H.Y.: Full-frame video stabilization with motion inpainting. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1150–1163 (2006)
Favorskaya, M., Buryachenko, V.: Video stabilization of static scenes based on robust detectors and fuzzy logic. Front. Artif. Intell. Appl. 254, 11–20 (2013)
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Favorskaya, M., Buryachenko, V. (2015). Fuzzy-Based Digital Video Stabilization in Static Scenes. In: Tsihrintzis, G., Virvou, M., Jain, L., Howlett, R., Watanabe, T. (eds) Intelligent Interactive Multimedia Systems and Services in Practice. Smart Innovation, Systems and Technologies, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-17744-1_5
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DOI: https://doi.org/10.1007/978-3-319-17744-1_5
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