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On visual ambiguities due to transparency in motion and stereo

  • Masahiko Shizawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 588)

Abstract

Transparency produces visual ambiguities in interpreting motion and stereo. Recent discovery of a general framework, principle of super-position, for building constraint equations of transparency makes it possible to analyze the mathematical properties of transparency perception. This paper theoretically examines multiple ambiguous interpretations in transparent optical flow and transparent stereo.

Keywords

Optical Flow Stereo Vision Flow Vector Stereo Match Constraint Line 
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.

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References

  1. 1.
    C.L.Fennema and W.Thompson: “Velocity Determination in Scenes Containing Several Moving Objects,” CGIP, Vol.9, pp.301–315(1979).Google Scholar
  2. 2.
    J.J.Little, H.Bülthoff and T.Poggio: “Parallel Optical Flow Using Local Voting,” Proc. 2nd ICCV, Tampa, FL, pp.454–459(1988).Google Scholar
  3. 3.
    R.Jasinschi, A.Rosenfeld and K.Sumi: “The Perception of Visual Motion Coherence and Transparency: a Statistical Model,” Tech. Rep. Univ. of Maryland, CAR-TR-512(1990).Google Scholar
  4. 4.
    D.J.Fleet and A.D.Jepson: “Computation of Component Image Velocity from Local Phase Information,” IJCV, Vol.5, No.1, pp.77–104(1990).Google Scholar
  5. 5.
    D.W.Murray and B.F.Buxton: “Scene Segmentation from Visual Motion Using Global Optimization,” IEEE Trans. PAMI, Vol.9, No.2, pp.220–228(1987).Google Scholar
  6. 6.
    B.G.Schunck: “Image Flow Segmentation and Estimation by Constraint Line Clustering,” IEEE Trans. PAMI, Vol.11, No.10, pp.1010–1027(1989).Google Scholar
  7. 7.
    E.H.Adelson and J.A.Movshon: “Phenomenal Coherence of Moving Visual Patterns,” Nature, 300, pp.523–525(1982).PubMedGoogle Scholar
  8. 8.
    G.R.Stoner, T.D.Albright and V.S.Ramachandran: “Transparency and coherence in human motion perception,” Nature, 344, pp.153–155(1990).PubMedGoogle Scholar
  9. 9.
    B.K.P.Horn and B.G.Schunck: “Determining Optical Flow,” Artificial Intelligence, Vol.17, pp.185–203(1981).Google Scholar
  10. 10.
    E.H.Adelson and J.R.Bergen: “Spatiotemporal Energy Models for the Perception of Motion,” J.Opt.Soc.Am.A, Vol.2, pp.284–299(1985).PubMedGoogle Scholar
  11. 11.
    J.G.Daugman: “Pattern and Motion Vision without Laplacian Zero Crossings,” J.Opt.Soc.Am.A, Vol.5, pp.1142–1148(1987).Google Scholar
  12. 12.
    D.J.Heeger: “Optical Flow Using Spatiotemporal Filters,” IJCV, 1, pp.279–302(1988).Google Scholar
  13. 13.
    S.Geman and D.Geman: “Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images,” IEEE Trans. PAMI, 6, pp.721–741(1984).Google Scholar
  14. 14.
    P.J.Besl, J.B.Birch and L.T.Watson: “Robust Window Operators,” Proc. 2nd ICCV, Tampa, FL, pp.591–600(1988).Google Scholar
  15. 15.
    A.Blake and A.Zisserman: Visual Reconstruction, MIT Press, Cambridge, MA(1987).Google Scholar
  16. 16.
    J.R.Bergen, P.Burt, R.Hingorani and S.Peleg: “Transparent-Motion Analysis,” Proc. 1st ECCV, Antibes, France, pp.566–569(1990).Google Scholar
  17. 17.
    A.L.Yuille and N.M.Grzywacz: “A Computational Theory for the Perception of Coherent Visual Motion,” Nature, 333, pp.71–74(1988).PubMedGoogle Scholar
  18. 18.
    N.M.Grzywacz and A.L.Yuille: “A Model for the Estimate of Local Image Velocity by Cells in the Visual Cortex,” Proc. Royal Society of London, Vol.B239, pp.129–161(1990).Google Scholar
  19. 19.
    D.Weinshall: “Perception of multiple transparent planes in stereo vision,” Nature, 341, pp.737–739(1989).PubMedGoogle Scholar
  20. 20.
    D.Weinshall: “The computation of multiple matching in stereo,” 14th European Conference on Visual Perception(ECVP), page A31(1990).Google Scholar
  21. 21.
    D.Marr: Vision, Freeman(1982).Google Scholar
  22. 22.
    R.Penrose: The Emperor's New Mind: Concerning Computers, Minds, and The Laws of Physics, Oxford University Press, Oxford(1989).Google Scholar
  23. 23.
    M.Shizawa and K.Mase: “Simultaneous Multiple Optical Flow Estimation,” Proc. 10th ICPR, Atlantic City, NJ, Vol.1, pp.274–278(June,1990).Google Scholar
  24. 24.
    -: “A Unified Computational Theory for Motion Transparency and Motion Boundaries Based on Eigenenergy Analysis,” Proc. IEEE CVPR'91, Maui, HI, pp.289–295(June,1991).Google Scholar
  25. 25.
    -: “Principle of Superposition: A Common Computational Framework for Analysis of Multiple Motion,” Proc. IEEE Workshop on Visual Motion, Princeton, NJ, pp.164–172(October,1991).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Masahiko Shizawa
    • 1
  1. 1.ATR Communication Systems Research LaboratoriesAdvanced Telecommunications Research Institute InternationalKyotoJapan

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