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Development of optical flow computation algorithms for strain measurement of solids
Lyubutin P.S.
Institute of Strength Physics and Materials Science of Siberian Branch Russian Academy of Sciences,
Tomsk Polytechnic University (National Research University)
DOI: 10.18287/0134-2452-2015-39-1-94-100
Full text of article: Russian language.
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Abstract:
This paper deals with the improvement of optical flow algorithms for strain measurements. The aim of the research is to reduce the computational effort and improve the robustness of the algorithms. The proposed modifications of the algorithms are based on the incremental approach to the estimation of the subsets displacement on the image subsequence, as well as on a three-dimensional recursive search (3DRS). An investigation of the robustness and performance of the algorithms shows the advantage of the proposed modifications.
Keywords:
displacement vector field, incremental approach, image processing.
Citation:
Lyubutin PS. Development of optical flow computation algorithms for strain measurement of solids. Computer Optics 2015; 39(1): 94-100. DOI: 10.18287/0134-2452-2015-39-1-94-100.
References:
- Barron, J.L. Performance of optical flow techniques / J.L. Barron, D.J. Fleet, S.S. Beauchemin // International Journal of Computer Vision. – 1994. – Vol. 12. – P. 43-77.
- Sutton, M.A. Image correlation for shape, motion and deformation measurements: basic concepts, theory and applications / M.A. Sutton, J.-J. Orteu, H. Schreier. – Springer, 2009. – 321 p.
- Horn, B.K.P. Determining optical flow / B.K.P. Horn, B.G. Schunck // Artificial Intelligence. – 1981. – Vol. 17. – P. 185-203.
- Lucas, B.D. An iterative image registration technique with an application to stereo vision / B.D. Lucas, T. Kanade // Proceedings of the 7th international joint conference on Artificial intelligence. – 1981. – Vol. 2. – P. 674-679.
- Lucas, B.D. Generalized Image Matching by the Method of Differences / B.D. Lucas. – Doctoral dissertation, tech. report. – Robotics Institute, Carnegie Mellon University, July, 1984. – 167 p.
- Fleet, D.J. Phase-based disparity measurement / D.J. Fleet, A.D. Jepson, M.R.M. Jenkin // CVGIP: Image Understanding. – 1991. – Vol. 53. – P. 198-210.
- Raffel, M. Particle Image Velocimetry: A Practical Guide / M. Raffel, C.E. Willert, S.T. Wereley, J. Kompenhans. – 2nd ed. – Springer, Berlin, 2007. – Vol. XX. – 448 p.
- Belyaev, E.A. Motion estimation algorithms for low bit-rate video compression / E.A. Belyaev, A.M. Turlikov // Computer Optics. – 2008. – Vol. 32(4). – P. 403-412. – (In Russian).
- Giachetti, A. The use of optical flow for road navigation / A. Giachetti, M. Campani, V. Torre // IEEE Transactions on Robotics and Automation. – 1998. – Vol. 14, Is. 1. – P. 34-48.
- Syryamkin, V.I. Television-optical technique for materials investigation and diagnostics of state of loaded materials and structure parts / V.I. Syryamkin, S.V. Panin // Computational Technologies. – 2003. – Vol. 8, Special issue. – P. 10-25. – (In Russian).
- Wang, C.-M. Estimating Optical Flow by Integrating Multi-Frame Information / C.-M. Wang, K.-C. Fan, C.-T. Wang // Journal of Information Science and Engineering. – 2008. – Vol. 24, Issue 6. – P. 1719-1731.
- Irani, M. Multi-Frame Optical Flow Estimation Using Subspace Constraints / M. Irani // Seventh International Conference on Computer Vision (ICCV'99). – 1999. – Vol. 1. – P. 626-633.
- Tao, M. Simple Flow: A Non-iterative, Sublinear Optical Flow Algorithm / M. Tao, J. Bai, P. Kohli, S. Paris // Computer Graphics Forum. –2012, May. – Vol. 31, Issue 2/1. – P. 345-353.
- Marzat, J. Real-time dense and accurate parallel optical flow using CUDA / J. Marzat, Y. Dumortier, A. Ducrot // Proceedings of the 7th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG). – 2009, 2-5 Feb. – P. 105-111.
- Durkovic, M. Performance of Optical Flow Techniques on Graphics Hardware / M. Durkovic, M. Zwick, F. Obermeier, K. Diepold // IEEE International Conference on Multimedia and Expo. – 2006. – P. 241-244.
- Braspenning, R.A. True-motion estimation using feature correspondence / R.A. Braspenning, G. de Haan // SPIE, Proceedings of Visual Communications and Image Processing. – 2004, Jan. – Vol. 5308. – P. 396-407.
- Lyubutin P.S. Investigation of accuracy and error rate performance of the displacement vector construction in evaluation of strains using TV-optical technique / P.S. Lyubutin, S.V. Panin // Computational Technologies. – 2006. – Vol. 11, Issue 2. – P. 52-66. – (In Russian).
- Panin, S.V. Application of the fractal dimension for estimating surface images obtained by various detectors / S.V. Panin, Yu.A. Altukhov, P.S. Lyubutin, A.V. Byakov, S.A. Khizhnyak // Optoelectronics, Instrumentation and Data Processing. – 2013. – Vol. 49, Issue 1. – P. 34-40.
- Panin, S.V. Study of Deformation and Fracture Using Data of Acoustic Emission, Correlation of Digital Images and Strain-Gauging / S.V. Panin, A.V. Byakov, P.S. Lyubutin, O.V. Bashkov, V.V. Grenke, I.V. Shakirov, S.A. Khizhnyak // Industrial Laboratory. – 2011. – Vol. 77, Is. 9. – P. 50-59.
- Voskoboinikov, Yu.E. Nonlinear algorithms for vector signal filtering / Yu.E. Voskoboinikov, V.G. Belyavtsev // Optoelectronics, Instrumentation and Data Processing. – 1999. – Vol. 35, Issue 5. – P. 29-38.
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