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Telecommunications and Radio Engineering
SJR: 0.203 SNIP: 0.44 CiteScore™: 1

ISSN Печать: 0040-2508
ISSN Онлайн: 1943-6009

Выпуски:
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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i9.40
pages 783-792

METHOD OF OPTICAL FLOW ESTIMATION BASED ON IMAGE BLOCK WEIGHTING

V. I. Kortunov
National Technical University "Kharkiv Polytechnic Institute", 2 Kyrpychova Str., Kharkiv, 61002, Ukraine
A. O. Molchanov
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
V. О. Gorokhovatskyi
Kharkiv National University of Radio Electronics, 14 Nauka Ave., Kharkiv, 61166, Ukraine

Краткое описание

A method of video camera (or platform) motion estimation used in image processing is proposed for determining the object motion parameters. The method defines and forms an optical flow with weighting measurements for the image block system. Numerical simulation data for test images obtained evidence on the efficiency and effectiveness of the approach offered. The developed method and algorithms for its implementation are versatile and can be applied in different fields of motion analysis and moving object control.

ЛИТЕРАТУРА

  1. Grishin, S.V., Vatolin, D.S., Lukin, A.S., Putilin, S.Yu. et al., (2008) , Review of block methods for motion estimation in digital video signals, Software Systems and Tools, 9, pp. 50-62, (in Russian).

  2. Molchanov, A., Kortunov, V., and Mohammadi F., (2017) , Estimation of accuracy in determining the translational velocity of a video camera using data from optical flow, Eastern-European Journal of Enterprise Technologies, 4/9(88), pp. 37-45.

  3. Gruzman, I.S., Kirichuk, V.S. et al., (2002) , Digital Image Processing in Information Systems, Novosibirsk, Russia: Published by NSTU, 352 p., (in Russian).

  4. Putilin, S., (2006) , Fast algorithm of motion search in video sequence, Proceedings of Graphicon Conference, Novosibirsk, Russia, pp.407-410, (in Russian).

  5. Hosur, P.I. and Ma, K.K., (1999) , Motion vector field adaptive fast motion estimation, Presented at the Second Int. Conf. Inf., Commun., Signal Process, Singapore, pp. 422-241.

  6. Horn, B. and Schunck, B., (1981) , Determining optical flow, Artificial Intelligence, 17, pp. 185-203.

  7. Ajvazyan, S.A. (ed.), Enyukov, I.S., and Meshalkin, L.D., (1985) , Applied Statistic: Investigation of Relationships, Moscow, Russia: Finansy i Statistika, 487 p., (in Russian).

  8. Tsypkin, Ya.Z., (1984) , Fundamentals of Information Identification Theory, Moscow, Russia: Nauka, 320 p., (in Russian).

  9. Gadetska, S.V. and Gorokhovatsky, V.O., (2018) , Statistical Measures for Computation of the Image Relevance of Visual Objects in the Structural Image Classification Methods, Telecommunications and Radio Engineering, 77(12), pp. 1041-1053.

  10. Lucas, B.D. and Kanade, T, (1981) , An Iterative Image Registration Technique with an Application to Stereo Vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence, 2, pp. 674-679.

  11. Verzhbitsky, V.M., (2000) , Numerical Methods (Linear Algebra and Nonlinear Equations), Moscow, Russia: Vysshaya Shkola, 266 p., (in Russian).

  12. Dikusar, V.V., (1998) , Some of numerical methods of linear algebraic equation solution, Soros Informative Journal, 9, pp. 111-120.

  13. Jing, Xuan and Lap-Pui Chau, (2004) , An Efficient Three-Step Search Algorithm for Block Motion Estimation, IEEE Transactions on Multimedia, 6(3), pp. 124-138.

  14. Ahn, Tae Gyoung, Yong Ho Moon, and Jae Ho Kim, (2004) , Fast Full-Search Motion Estimation Based on Multilevel Successive Elimination Algorithm, IEEE Transactions on Circuits and Systems for Video Technology, 14(11), pp. 202-218.


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