Advertisement

Motion Integration Using Competitive Priors

  • Shuang Wu
  • Hongjing Lu
  • Alan Lee
  • Alan Yuille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5604)

Abstract

Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation [5][9]. These findings are inconsistent with standard models of motion integration which predict best performance for translation. To explain this discrepancy, our theory formulates motion perception at two levels of inference: we first perform model selection between the competing models (e.g. translation, rotation, and expansion) and then estimate the velocity using the selected model. We define novel prior models for smooth rotation and expansion using techniques similar to those in the slow-and-smooth model [23] (e.g. Green functions of differential operators). The theory gives good agreement with the trends observed in four human experiments.

Keywords

Green Function Radial Motion Rotation Model Translation Model Psychophysical Experiment 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barraza, J.F., Grzywacz, N.M.: Measurement of angular velocity in the perception of rotation. Vision Research 42 (2002)Google Scholar
  2. 2.
    Black, M.J., Anandan, P.: The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields. Computer Vision and Image Understanding 63(1), 75–104 (1996)CrossRefGoogle Scholar
  3. 3.
    Duchon, J.: In: Schempp, W., Zeller, K. (eds.). Lecture Notes in Math., vol. 571, pp. 85–100. Springer, Berlin (1977)Google Scholar
  4. 4.
    Duffy, C.J., Wurtz, R.H.: Sensitivity of MST neurons to optic flow stimuli. I. A continuum of response selectivity to large field stimuli. Journal of Neurophysiology 65, 1329–1345 (1991)Google Scholar
  5. 5.
    Freeman, T., Harris, M.: Human sensitivity to expanding and rotating motion: effect of complementary masking and directional structure. Vision research 32 (1992)Google Scholar
  6. 6.
    Hildreth, E.C.: Computations Underlying the Measurement of Visual Motion. Artificial Intelligence 23(3), 309–354 (1984)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Horn, B., Schunck, B.: Determining Optical Flow. Artificial Intelligence 17 (1981)Google Scholar
  8. 8.
    Knill, D., Richards, W. (eds.): Perception as Bayesian Inference. Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  9. 9.
    Lee, A., Yuille, A., Lu, H.: Superior perception of circular/radial than translational motion cannot be explained by generic priors. In: VSS 2008 (2008)Google Scholar
  10. 10.
    Lu, H., Yuille, A.L.: Ideal Observers for Detecting Motion: Correspondence Noise. In: NIPS 2005 (2005)Google Scholar
  11. 11.
    Morrone, M.C., Burr, D.C., Vaina, L.: Two stages of visual processing for radial and circular motion. Nature 376, 507–509 (1995)CrossRefGoogle Scholar
  12. 12.
    Morrone, M., Tosetti, M., Montanaro, D., Fiorentini, A., Cioni, G., Burr, D.C.: A cortical area that responds specifically to optic flow revealed by fMRI. Nature Neuroscience 3, 1322–1328 (2000)CrossRefGoogle Scholar
  13. 13.
    Nishida, S., Amano, K., Edwards, M., Badcock, D.R.: Global motion with multiple Gabors - A tool to investigate motion integration across orientation and space. In: VSS 2006 (2006)Google Scholar
  14. 14.
    Ramachandran, V.S., Anstis, S.M.: The perception of apparent motion. Scientific American 254, 102–109 (1986)CrossRefGoogle Scholar
  15. 15.
    Roth, S., Black, M.J.: On the Spatial Statistics of Optical Flow. In: International Conference of Computer Vision (2005)Google Scholar
  16. 16.
    Sekuler, R., Watamaniuk, S.N.J., Blake, R.: Perception of Visual Motion. In: Pashler, H. (ed.) Steven’s Handbook of Experimental Psychology, 3rd edn., J. Wiley Publishers, New York (2002)Google Scholar
  17. 17.
    Stocker, A.A., Simoncelli, E.P.: Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience 9(4), 578–585 (2006)CrossRefGoogle Scholar
  18. 18.
    Stocker, A.A., Simoncelli, E.: A Bayesian model of conditioned perception. In: Proceedings of Neural Information Processing Systems (2007)Google Scholar
  19. 19.
    Tanaka, K., Fukada, Y., Saito, H.: Underlying mechanisms of the response specificity of expansion/contraction and rotation cells in the dorsal part of the MST area of the macaque monkey. Journal of Neurophysiology 62, 642–656 (1989)Google Scholar
  20. 20.
    Ullman, S.: The Interpretation of Structure from Motion. PhD Thesis. MIT (1979)Google Scholar
  21. 21.
    Weiss, Y., Adelson, E.H.: Slow and smooth: A Bayesian theory for the combination of local motion signals in human vision Technical Report 1624. Massachusetts Institute of Technology (1998)Google Scholar
  22. 22.
    Weiss, Y., Simoncelli, E.P., Adelson, E.H.: Motion illusions as optimal percepts. Nature Neuroscience 5, 598–604 (2002)CrossRefGoogle Scholar
  23. 23.
    Yuille, A.L., Grzywacz, N.M.: A computational theory for the perception of coherent visual motion. Nature 333, 71–74 (1988)CrossRefGoogle Scholar
  24. 24.
    Yuille, A.L., Grzywacz, N.M.: A Mathematical Analysis of the Motion Coherence Theory. International Journal of Computer Vision 3, 155–175 (1989)CrossRefGoogle Scholar
  25. 25.
    Yuille, A.L., Ullman, S.: Rigidity and Smoothness of Motion: Justifying the Amoothness Assumption in Motion Measurement. In: Ullman, S., Richards, W. (eds.) Image Understanding 1989, ch. 8 (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shuang Wu
    • 1
  • Hongjing Lu
    • 2
  • Alan Lee
    • 2
  • Alan Yuille
    • 1
  1. 1.Department of StatisticsUCLAUSA
  2. 2.Department of PsychologyUCLAUSA

Personalised recommendations