SciELO - Scientific Electronic Library Online

 
vol.18 número4Designing Minimal Sorting Networks Using a Bio-inspired TechniqueWikification of Learning Objects Using Metadata as an Alternative Context for Disambiguation índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Computación y Sistemas

versão On-line ISSN 2007-9737versão impressa ISSN 1405-5546

Comp. y Sist. vol.18 no.4 Ciudad de México Out./Dez. 2014

https://doi.org/10.13053/CyS-18-4-1557 

Artículos regulares

 

Periodicity-Based Computation of Optical Flow

 

Georgii Khachaturov, Silvia Beatriz González Brambila, and Jesús Isidro González Trejo

 

Departamento de Sistemas, Universidad Autónoma Metropolitana (Azcapotzalco). Mexico. xgeorge@correo.azc.uam.mx

 

Article received on 27/09/2013.
Accepted on 27/06/2014.

 

Abstract

The standard Brightness Constancy Equation states spatiotemporal shift invariance of the input data along a local velocity of optical flow. In its turn, the shift invariance leads to a periodic function of a real argument. This allows application of a known test for periodicity to computation of optical flow at random locations. The approach is valid also for higher dimensions: for example, it applies to a sequence of 3D tomography images. The proposed method has a reasonably high accuracy for continuous flow and is noise tolerant. Special attention is paid to weak signal input. It is shown that a drastic reduction in the signal strength worsens the accuracy of estimates insignificantly. For a possible application to tomography, this would lead to an unprecedented diminution of harmful radiation exposure.

Keywords. Optical flow, periodicity-based processing, preventive tomography, night vision.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

References

1. Anandan, P. (1989). A computational framework and an algorithm for the measurement of visual motion. IJCV, Vol. 2, No.3, pp. 283-310.         [ Links ]

2. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M. J., & Szeliski, R. (2011). A Database and Evaluation Methodology for Optical Flow. IJCV, Vol. 92, No. 1, pp.1-31.         [ Links ]

3. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., & Szeliski, R. (2013). Middlebury open evaluation system [online]         [ Links ].

4. Barron, J.L., Fleet, D.J., & Beauchemin, S. (1994). Performance of optical flow techniques. IJCV, Vol. 12, No. 1, pp. 43-77.         [ Links ]

5. Black, M.J. (2013). The Yosemite dataset provided with ground truth [online]         [ Links ].

6. Brox, T. & Malik, J. (2010). Large displacement Optical Flow: descriptor matching in variational motion estimation, IEEE TPAMI, doi: 10.1109/TPAMI.2010.143.

7. Bruhn, A., Weickert, J., & Schnorr, C. (2005). Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. IJCV, Vol. 61, No. 3, pp. 211-231.         [ Links ]

8. Fischler, M. & Bolls, R. (1981). Random sample consensus: A paradigm for fitting with application to image analysis and automated cartography. Communication of the ACM, Vol. 24, No. 6, pp. 381-395.         [ Links ]

9. Fleet, D.J. & Jepson, A.D. (1990). Computation of component image velocity from local phase information. IJCV. Vol. 5, pp. 77-104.         [ Links ]

10. Goldluecke, B. & Cremers, D. (2010). Convex Relaxation for Multilabel Problems with Product Label Spaces. Proc. of European Conf. on Computer Vision (ECCV-2010).         [ Links ]

11. Heeger, D.J. (1987). Model for the extraction of image flow. J. Opt. Soc. Am. A 4, pp. 1455-1471.         [ Links ]

12. Horn, B. (1986). Robot vision. Cambridge: MIT Press.         [ Links ]

13. Horn, B. & Schunck, B.G. (1981). Determining optical flow. Artificial Intelligence, Vol. 17, pp. 185-203.         [ Links ]

14. Horowitz, P., & Hill, W. (1989). The Art of Electronics. 2nd edition. Cambridge (UK): Cambridge University Press.         [ Links ]

15. Hörmann, W., Leydold, J., & Derflinger, G. (2004). Automatic nonuniform random variate generation. Springer.         [ Links ]

16. Jepson, A., & Black, M.J. (1993). Mixture models for optical flow computation. CVPR, pp. 760-761.         [ Links ]

17. Jojic, N., & Frey, B. (2001). Learning flexible sprites in video layers. CVPR, Vol. 1, pp. 199-206.         [ Links ]

18. Khachaturov, G. (1995). An approach to detection of line elements. Proc. of the Second Asian Conference on Computer Vision (ACCV'95), Vol. 3, pp. 559-563.         [ Links ]

19. Khachaturov, G. (2011). A scalable, high-precision, and low-noise detector of shift-invariant image locations. Pat. Rec. Letters, Vol. 32, pp. 145-152, doi: 10.1016/j.patrec.2010.10.002.         [ Links ]

20. Lucas, B.D. & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proc. of DAPRA Imaging Understanding Workshop, pp. 121-130.         [ Links ]

21. Marr, D. & Ullman, S. (1981). Directional selectivity and its use in early visual processing. Proc. Roy. Soc., London B 211, pp. 151-180.         [ Links ]

22. Nagel, H.H. (1989). On a constraint equation for the estimation of displacement rates in image sequences. IEEE Trans PAMI, Vol. 11, pp. 13-30.         [ Links ]

23. Otte, M. & Nagel, H.H. (1994). Optical flow estimation: advances and comparisons. Proc. of the European conference on computer vision, pp. 51-60.         [ Links ]

24. Owens, J., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A., & Purcell, T. (2007). A Survey of General-Purpose Computation on Graphics Hardware. Computer Graphics Forum, Vol. 26, No. 1, pp. 80-113.         [ Links ]

25. Rao, C.R., Toutenburg, H., Fieger, A., Heumann, C., Nittner, T., & Scheid, S. (1999). Linear Models: Least Squares and Alternatives. Springer Series in Statistics.         [ Links ]

26. Reddy, B.S. & Chatterji, B.N. (1996). An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. on Image Processing, Vol. 5, No. 8, pp. 1266-1271.         [ Links ]

27. Scharstein, D. (2013). Open source code for the flow representation in the colour-coding format [online]         [ Links ].

28. Scharstein, D. & Szeliski, R. (2003). High-accuracy stereo depth maps using structured light. Proc. of the IEEE conference on computer vision and pattern recognition, pp. 195-202.         [ Links ]

29. Seitz, S., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. Proc. of the IEEE conference on computer vision and pattern recognition. Vol. 1, pp. 519-526.         [ Links ]

30. Singh. (1990). An estimation theoretic framework for image-flow computation. Proc. of ICCV, Osaka, pp. 168-177.         [ Links ]

31. Sudderth, E., Sun, D., & Black, M. (2012). Layered Segmentation and Optical Flow Estimation over Time. CVPR, doi: 10.1109/CVPR.2012.6247873.

32. Tagliasacchi, M. (2007). A Genetic Algorithm for Optical Flow Estimation. Image and Vision Computing, Vol. 25, pp. 141-147, doi: 10.1016/j.imavis.2006.01.021.         [ Links ]

33. Uras, S., Girosi, F., Verri, A., & Torre, V. (1988). A computational approach to motion perception. Biol. Cybern., Vol. 60, pp. 79-87.         [ Links ]

34. Werlberger, M., Pock, T., & Bischof, H. (2010). Motion estimation with non-local total variation regularization. Proc. of IEEE Conference CVPR-2010.         [ Links ]

35. Xu, L., Chen, J., & Jia, J. (2008). A segmentation based variational model for accurate optical flow estimation. Proc. of the ECCV-2008, Vol. 1, pp. 671 -684.         [ Links ]

36. Zimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., & Seidel, H.P. (2009). Complementary optic flow. Proc. of Int. Conf. on Energy Minimization Methods in Comp.Vis. and Pat.Rec. (EMMCVPR).         [ Links ]

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons