УДК: 004.932.2
Using microelectromechanical systems when solving the problem of digital stabilization of video images
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Publication in Journal of Optical Technology
Иванов П.И. Использование микроэлектромеханических систем при решении задачи стабилизации видеоизображений // Оптический журнал. 2015. Т. 82. № 8. С. 84–91.
Ivanov P.I. Using microelectromechanical systems when solving the problem of digital stabilization of video images [in Russian] // Opticheskii Zhurnal. 2015. V. 82. № 8. P. 84–91.
P. I. Ivanov, "Using microelectromechanical systems when solving the problem of digital stabilization of video images," Journal of Optical Technology. 82(8), 557-562 (2015). https://doi.org/10.1364/JOT.82.000557
This article proposes a method for using a video camera and the data obtained from microelectromechanical systems in combination to solve the image-stabilization problem. An algorithm is presented for choosing the comparison regime on the basis of an estimate of the change of position of the optical axis during exposure of a frame. A modification was made in the procedure for comparing the key points and filtering the errors by the RANSAC method, using the readings of the systems mentioned above as auxiliary information. The results of the operation of known methods of digital stabilization of images are compared with the proposed algorithm, which was effective when video images were used in combination with the data of microelectromechanical systems to solve the stabilization problem.
microelectromechanical systems, digital stabilization of images, moving base, motion estimation, SURF, FRAK, RANSAC
OCIS codes: 100.2960
References:1. A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint Kalman filter for vision-aided inertial navigation,” in IEEE International Conference on Robotics and Automation, Rome, Italy, 2007, pp. 3565–3572.
2. C. Jia and B. L. Evans, “Probabilistic 3D motion estimation for rolling-shutter video rectification from visual and inertial measurements,” in IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), Banff, Canada, 2012, pp. 203–208.
3. G. Hanning, N. Forslöw, P. E. Forssén, E. Ringaby, D. Törnqvist, and J. Callmer, “Stabilizing cell-phone video using inertial measurement sensors,” in IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain, 2011, pp. 1–8.
4. A. Karpenko, D. Jacobs, J. Baek, and M. Levoy, “Digital video stabilization and rolling-shutter correction using gyroscopes,” Tech. Rep. CTSR 2011-03 (Stanford University, 2011).
5. S. P. Zvantsev, P. I. Ivanov, and Yu. E. Merzlyutin, “Digital stabilization of images under conditions of planned movement,” J. Opt. Technol. 79, 721 (2012) [Opt. Zh. 79, No. 11, 59 (2012)].
6. N. N. Lapina, V. R. Lutsiv, I. A. Malyshev, and A. S. Potapov, “Features of image comparison in problems of determining the location of a mobile robot,” J. Opt. Technol. 77, 677 (2010) [Opt. Zh. 77, No. 11, 25 (2010)].
7. A. S. Potapov, I. A. Malyshev, A. E. Puysha, and A. N. Averkin, “New paradigm of learnable computer vision algorithms based on the representational MDL principle,” Proc. SPIE 7696, 769606 (2010).
8. H. Bay, “Speeded-up robust features (SURF),” Comput. Vis. Image Understanding 110, No. 3, 346 (2008).
9. A. Alahi, R. Ortiz, and P. Vandergheynst, “FREAK: Fast Retina Keypoint,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012, pp. 510–517.
10. A. N. Averkin, I. P. Gurov, M. V. Peterson, and A. S. Potapov, “Spectral-differential feature matching and clustering for multi-body motion estimation,” IAPR Conference on Machine Vision Applications (MVA2011), Nara, Japan, 13–15 June 2011, pp. 173–176.
11. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381 (1981).