ON PARALLEL IMPLEMENTATION OF HORN AND SCHUNCK MOTION ESTIMATION METHOD

Fella Charif, Noureddine Djedi, and Abderrazak Benchabane

References

  1. [1] J.L. Barron, D.J. Fleet, and S.S. Beauchemin, Performance of optical flow techniques, International Journal of Computer Vision, 12(1), 1994, 43–77.
  2. [2] B.K.P. Horn and B.G. Schunck, Determining optical flow, Artificial Intelligence, 17, 1981, 185–204.
  3. [3] A. Mitiche and A.R. Mansouri, On convergence of the Horn and Schunck optical-flow computation, IEEE Image Processing, 13, 2004, 848–852.
  4. [4] J.L. Mart´ın, A. Zuloaga, C. Cuadrado, J. L´azaro, and U. Bidarte, Hardware implementation of optical flow constraint equation using FPGAs, Computer Vision and Image Understanding, 98, 2005, 462–490.
  5. [5] A. Zuloaga, J.L. Mart´ın, and J. Ezquerra, Hardware architecture for optical flow estimation in real time, Proc. ICIP, 3, 1998, 972–976.
  6. [6] P. Cobos and F. Monasterio, FPGA implementation of the Horn & Schunk optical flow algorithm for motion detection in real time images, Dcis’98 Proc., XIII Design of Circuits and Systems Conference, 1998, 616–621.
  7. [7] P. Cobos and F. Monasterio, FPGA implementation of camus correlation optical flow algorithm for real time images, 14th Int. Conf. on Vision Interface Proceedings, 2001, 32–38.
  8. [8] Y. Zhang, W. Ma, and C. Yi, The link between Newton iteration for matrix inversion and Zhang neural network (ZNN), Proc. IEEE Int. Conf. on Industrial Technology, Chengdu, 2008, 1–6.
  9. [9] Y. Zhang, W. Ma, and B. Cai, From Zhang neural network to Newton iteration for matrix inversion, IEEE Transactions on Circuits and Systems, 56(7), 2009, 1405–1415.
  10. [10] Y. Zhang, X. Guo, W. Ma, K. Chen, and B. Cai, MATLAB Simulink modeling and simulation of Zhang neural network for online time-varying matrix inversion, Proc. IEEE Int. Conf.on Networking, Sensing and Control, 2008, 1480–1485.
  11. [11] B. Svensson and T. Nordstr¨om, Using and designing massively parallel computers for artificial neural networks, Journal of Parallel and Distributed Computing, 14, 1992, 260–285.
  12. [12] E.P. Simoncelli, Design of multi-dimensional derivative filters, IEEE Conf. on Image Processing, 1, 1994, 790–793.
  13. [13] F. Charif and Z.-E. Baarir, A fast modified Horn & Schunck method, IEEE Int. Symp. on Industrial Electronics, 2006, 526–531.
  14. [14] S. Baker, D. Scharstein, J. Lewis, S. Roth, M.J. Black, and R. Szeliski, A database and evaluation methodology for optical flow, Proc. 11th IEEE Int. Conf. on Computer Vision (ICCV), 2007, 1–8, http://vision.middlebury.edu/flow/.
  15. [15] J.C. Sosa, J.A. Boluda, F. Pardo, and R. G´omez-Fabela, Change-driven data flow image processing architecture for optical flow computation, Journal of Real-Time Image Processing, 2(4), 2007, 259–270.

Important Links:

Go Back