Measuring the ‘Rubber Rhomboid’ effect

  • David Clements
  • Roddy Cowie
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)

Abstract

The ‘Rubber Rhomboid’ effect is an illusion where a rigid skeleton parallelepiped is rotated and appears to undergo rubbery deformation [1]. This paper reports the first attempt to map the effect systematically. Three subjects assessed the level of deformation of computer generated rotating parallelepipeds. The main finding was that objects containing equal angles at a vertex were more stable across all orientations, especially when the equal angles were 90°. The findings are discussed in the context of computational accounts of human vision.

Keywords

Rubber Wallach 

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References

  1. 1.
    Cowie R. Rubber Rhomboids: Nonrigid interpretation of a rigid structure moving. Perception and Psychophysics 1987; 42: 407–408CrossRefGoogle Scholar
  2. 2.
    Roberts RG. Machine perception of 3-Dimensional solids. In: Tippett JT, Berkowitz DA, Clapp LC, Koester C J and Vandenburg A (eds) Optical and Electro-optical Information Processing. MIT Press Cambridge, Mass., 1965Google Scholar
  3. 3.
    Guzman A. Decomposition of a visual scene into three-dimensional bodies. In IAFIPS Conference Proceedings 33. Thompson, Washington, DC 1968, pp 291304Google Scholar
  4. 4.
    Huffman D. Impossible objects as nonsense sentences. In: Meltzer B and Michie D (eds) Machine Intelligence (Vol. 6). Edinburgh University Press Edinburgh, 1971Google Scholar
  5. 5.
    Clowes M. On seeing things. Artificial Intelligence 1971; 2: 79–116CrossRefGoogle Scholar
  6. 6.
    Michie DM. On not seeing things. In On Machine Intelligence. John Wiley New York, 1974Google Scholar
  7. 7.
    Barrow HG and Tenebaum JM. Recovering intrinsic scene characteristics from images. In: Hanson AR and Riseman E M (eds) Computer vision systems. Academic Press New York, 1978Google Scholar
  8. 8.
    Horn BKP. Determining lightness from an image. Computer Graphics and Image Processing 1974; 3: 277–299CrossRefGoogle Scholar
  9. 9.
    Horn BKP. Obtaining shape from shading information. In: Winston PH (ed) The psychology of Computer vision. McGraw-Hill London, 1975Google Scholar
  10. 10.
    Horn BKP. Image intensity understanding. Artificial Intelligence 1977; 8: 201–231MATHCrossRefGoogle Scholar
  11. 11.
    Man D. Early processing of visual information. Philosophical Transactions Royal Society London 1976; B 275: 483–524CrossRefGoogle Scholar
  12. 12.
    Marr D. Analysis of occluding contour. Proceedings of the Royal Society London 1977; B 197: 441–475CrossRefGoogle Scholar
  13. 13.
    Mari D. Artificial intelligence-a personal view. Artificial Intelligence 1977; 9: 37–48CrossRefGoogle Scholar
  14. 14.
    Mari D. Representing visual information-a computational approach. In: Hanson AR and Riseman EM (eds) Computer vision systems. Academic press New York, 1978Google Scholar
  15. 15.
    Marr D. Vision. WH Freeman San Francisco, 1982Google Scholar
  16. 16.
    Ullman S. The Interpretation of Visual Motion. MIT Press Cambridge, Mass., 1979Google Scholar
  17. 17.
    Longuet-Higgins HC and Prazdny K. The interpretation of a moving retinal image. Proceedings of the Royal Society London 1980; B 208: 385–397CrossRefGoogle Scholar
  18. 18.
    Longuet-Higgins HC. A computer algorithm for reconstructing a scene from two projections. Nature 1981; 293: 433–435CrossRefGoogle Scholar
  19. 19.
    Cowie R. The alternatives allowed by a rectangular postulate, and a pragmatic approach to interpreting motion. In: Hallam J and Mellish C (eds) Advances in Artificial Intelligence.Wiley Chichester, 1987Google Scholar
  20. 20.
    Longuet-Higgins HC. The visual ambiguity of a moving plane. Proceedings of the Royal Society London 1984; B 223: 165–175CrossRefGoogle Scholar
  21. 21.
    Scott GL. Local and Global Processing of Moving Images. Pitman London, 1988Google Scholar
  22. 22.
    Subbarao M. Interpretation of Visual Motion: a computational study. Pitman London, 1988Google Scholar
  23. 23.
    Poggio T, Torre V and Koch C. Computational Vision and regularization theory. Nature 1985; 317: 314–319CrossRefGoogle Scholar
  24. 24.
    Ames A. Visual Perception and the Rotating Trapezoid Window. Psychological Monographs 1951; 65: whole number 324Google Scholar
  25. 25.
    Cowie RID. Rotating trapezia which appear transparent and luminous during apparent reversals. Perception 1989; 18: 173–180CrossRefGoogle Scholar
  26. 26.
    Wallach H and O Connell DN. The kinetic depth effect. Journal of Experimental Psychology 1953; 45: 205–217CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • David Clements
  • Roddy Cowie

There are no affiliations available

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