Optic Flow Integration at Multiple Spatial Frequencies – Neural Mechanism and Algorithm

  • Cornelia Beck
  • Pierre Bayerl
  • Heiko Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


In this work we present an iterative multi-scale algorithm for motion estimation that follows mechanisms of motion processing in the human brain. Keeping the properties of a previously presented neural model of cortical motion integration we created a computationally fast algorithmic implementation of the model. The novel contribution is the extension of the algorithm to operate on multiple scales without the disadvantages of typical coarse-to-fine approaches. Compared to the implementation with one scale our multi-scale approach generates faster dense flow fields and reduces wrong motion estimations. In contrast to other approaches, motion estimation on the fine scale is biased by the coarser scales without being corrupted if erroneous motion cues are generated on coarser scales, e.g., when small objects are overlooked. This multi-scale approach is also consistent with biological observations: The function of fast feedforward projections to higher cortical areas with large receptive fields and feedback connections to earlier areas as suggested by our approach might contribute to human motion estimation.


Input Image Motion Estimation Neural Model Coarse Scale Feedback Connection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ungerleider, L.G., Haxby, J.V.: ’What’ and ’where’ in the human brain. Current Opinion in Neurobiology 4, 157–165 (1994)CrossRefGoogle Scholar
  2. 2.
    Beauchemin, S.S., Barron, J.L.: The Computation of Optical Flow. ACM Computing Surveys 27(3), 433–467 (1995)CrossRefGoogle Scholar
  3. 3.
    Bayerl, P., Neumann, H.: Disambiguating Visual Motion through Contextual Feedback Modulation. Neural Computation 16(10), 2041–2066 (2004)zbMATHCrossRefGoogle Scholar
  4. 4.
    Hupé, J.M., James, A.C., Girard, P., Lomber, S.G., Payne, B.R., Bullier, J.: Feedback Connections Act on the Early Part of the Responses in Monkey Visual Cortex. J. Neurophys. 85, 134–145 (2001)Google Scholar
  5. 5.
    Bayerl, P., Neumann, H.: Towards real-time: A neuromorphic algorithm for recurrent motion segmentation. In: Ninth International Conference on Cognitive and Neural Systems (ICCNS 2005), Boston, USA (2005)Google Scholar
  6. 6.
    Horn, B.K.P., Schunk, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  7. 7.
    Weiss, Y., Fleet, D.J.: Velocity likelihoods in biological and machine vision, Probabilistic models of the brain: Perception and neural function, pp. 81–100. MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    Pack, C.C., Born, R.T.: Temporal dynamics of a neural solution to the aperture problem in cortical area MT. Nature 409, 1040–1042 (2001)CrossRefGoogle Scholar
  9. 9.
    Adelson, E., Bergen, J.: Spatiotemporal energy models for the perception of motion. Optical Society of America A 2(2), 284–299 (1985)CrossRefGoogle Scholar
  10. 10.
    Simoncelli, E.: Course-to-fine Estimation of Visual Motion. In: IEEE Eighth Workshop on Image and Multidimensional Signal Processing, Cannes France (September 1993)Google Scholar
  11. 11.
    Burt, P.J., Adelson, E.H.: The Laplacian Pyramid as a Compact Image Code. IEEE Transactions On Communications 31(4) (1983)Google Scholar
  12. 12.
    Bar, M.: A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition. Journal of Cognitive Neuroscience 15(4), 600–609 (2003)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Stein, F.: Efficient Computation of Optical Flow Using the Census Transform. In: DAGM-Symposium 2004, pp. 79–86 (2004)Google Scholar
  14. 14.
    Albright, T.D.: Direction and orientation selectivity of neurons in visual area MT of the macaque. J. Neurophys. 52, 1106–1130 (1984)Google Scholar
  15. 15.
    Duffy, C.J., Wurtz, R.H.: Sensitivy of MST Neurons to Optic Flow Stimuli. I. A Continuum of Response Selectivity to Large-Field Stimuli. J. Neurophys. 65, 1329–1345 (1991)Google Scholar
  16. 16.
    Lamme, V.A.F., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23, 571–579 (2000)CrossRefGoogle Scholar
  17. 17.
    Torralba, A., Oliva, A.: Statistics of natural image categories. Network: Comput. Neural Syst. 14, 391–412 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cornelia Beck
    • 1
  • Pierre Bayerl
    • 1
  • Heiko Neumann
    • 1
  1. 1.Dept. of Neural Information ProcessingUniversity of UlmGermany

Personalised recommendations