Separating Global Motion Components in Transparent Visual Stimuli – A Phenomenological Analysis

  • Andrew Meso
  • Johannes M. Zanker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


When two distinct movements overlap in the same region of the visual field, human observers may perceive motion transparency. This perception requires the visual system to separate informative and non informative motion signals into transparent components. In this study, we explored the computational constraints in solving this signal separation task - particularly for the stimulus configuration where two grating components move in the same direction at different speeds. We developed a phenomenological model which demonstrates that separation can be achieved only for stimuli with a broadband Fourier spectrum. The model identifies the informative component signals from the non informative signals by considering edges. This approach is shown to be limited by an edge sensitive spatial filtering of the image sequence, the threshold tolerance for local signals considered and the number of iterative computational steps.


Motion transparency Fourier spectra Global motion Signal separation 


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  1. 1.
    Snowden, R.J., Verstraten, F.A.J.: Motion transparency: making models of motion transparency transparent. Trends in Cognitive Sciences 3(10), 369–377 (1999)CrossRefGoogle Scholar
  2. 2.
    Qian, N., Andersen, R.A., Adelson, E.H.: Transparent Motion Perception as Detection of Unbalanced Motion Signals. III. Modeling. Journal of Neuroscience 14, 7381–7392 (1994)Google Scholar
  3. 3.
    Qian, N., Andersen, R.A., Adelson, E.H.: Transparent Motion Perception as Detection of Unbalanced Motion Signals. I. Psychophysics. Journal of Neuroscience 14, 7357–7366 (1994)Google Scholar
  4. 4.
    Qian, N., Andersen, R.A.: Transparent Motion Perception as Detection of Unbalanced Motion Signals. II. Physiology. Journal of Neuroscience 14, 7367–7380 (1994)Google Scholar
  5. 5.
    Treue, S., Hol, K., Rauber, H.-J.: Seeing multiple directions of motion - physiology and psychophysics. Nature Neuroscience 3, 270–276 (2000)CrossRefGoogle Scholar
  6. 6.
    Nowlan, S.J., Sejnowski, T.J.: Filter selection model for motion segmentation and velocity integration. Journal of the Optical Society of America A11, 3177–3200 (1994)Google Scholar
  7. 7.
    Koechlin, E., Anton, J.L., Burnod, Y.: Bayesian interference in populations of cortical neurons: a model of motion integration and segmentation in area MT. Biological Cybernetics 80, 25–44 (1999)zbMATHCrossRefGoogle Scholar
  8. 8.
    Zanker, J.M.: A computational analysis of separating motion signals in transparent random dot kinematograms. Spatial Vision 18(4), 431–445 (2005)CrossRefGoogle Scholar
  9. 9.
    Jasinschi, R., Rosenfeld, A., Sumi, K.: Perceptual motion transparency: the role of geometrical information. Journal of the Optical Society of America A 9, 1865–1879 (1992)CrossRefGoogle Scholar
  10. 10.
    Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Freeman & Co., San Francisco (1982)Google Scholar
  11. 11.
    Del Viva, M.M., Morrone, M.C.: Motion analysis by feature tracking. Vision Research 38, 3633–3653 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrew Meso
    • 1
  • Johannes M. Zanker
    • 1
  1. 1.Computational vision Laboratory, PsychologyRoyal Holloway University of LondonEngland

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