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The generic viewpoint assumption in a framework for visual perception


A VISUAL system makes assumptions in order to interpret visual data. The assumption of 'generic view'1–4 states that the observer is not in a special position relative to the scene. Researchers commonly use a binary decision of generic or accidental view to disqualify scene interpretations that assume accidental viewpoints5–10. Here we show how to use the generic view assumption, and others like it, to quantify the likelihood of a view, adding a new term to the probability of a given image interpretation. The resulting framework better models the visual world and reduces the reliance on other prior assumptions. It may lead to computer vision algorithms of greater power and accuracy, or to better models of human vision. We show applications to the problems of inferring shape, surface reflectance properties, and motion from images.

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  1. Koenderink, J. J. & van Doorn, A. J. Biol. Cybern. 32, 211–216 (1979).

    Article  CAS  Google Scholar 

  2. Binford, T. O. Art. Intel. 17, 205–244 (1981).

    Article  Google Scholar 

  3. Biederman, I. Comp. Vis. Graph. Image Proc. 32, 29–73 (1985).

    Article  Google Scholar 

  4. Nakayama, K. & Shimojo, S. Science 257, 1357–1363 (1992).

    Article  ADS  CAS  Google Scholar 

  5. Lowe, D. G. & Binford, T. O. IEEE Pat. Anal. Mach. Intel. 7, 320–326 (1985).

    Article  CAS  Google Scholar 

  6. Malik, J. Intl. J. Comp. Vis. 1, 73–103 (1987).

    Article  Google Scholar 

  7. Richards, W. A., Koenderink, J. J. & Hoffman, D. D. J. Opt. Soc. Am. A. 4, 1168–1175 (1987).

    Article  ADS  Google Scholar 

  8. Pentland, A. P. Intl. J. Comp. Vis. 1, 153–162 (1990).

    Article  Google Scholar 

  9. Leclerc, Y. G. & Bobick, A. F. in Proc. IEEE Computer Vision and Pattern Recognition 552–558 (Maui, Hawaii, 1991).

    Google Scholar 

  10. Jepson, A. D. & Richards, W. Spatial Vision in Humans and Robots (eds Harris, L. & Jenkin, M.) (Cambridge Univ. Press, UK, 1992).

    Google Scholar 

  11. Berger, J. O. Statistical Decision Theory and Bayesian Analysis (Springer, New York, 1985).

    Book  Google Scholar 

  12. Tikhonov, A. N. & Arsenin, V. Y. Solutions of III-posed Problems (Winston, Washington DC, 1977).

    MATH  Google Scholar 

  13. Poggio, T., Torre, V. & Koch, C. Nature 317, 314–319 (1985).

    Article  ADS  CAS  Google Scholar 

  14. Terzopoulos, D. IEEE Pat. Anal. Mach. Intell. 8, 413–424 (1986).

    Article  Google Scholar 

  15. Szeliski, R. Bayesian Modeling of Uncertainty in Low-level Vision (Kluwer Academic, Boston, 1989).

    Book  Google Scholar 

  16. Marr, D. C. Vision (Freeman, New York, 1982).

    Google Scholar 

  17. MacKay, D. J. C., Neural Comput. 4, 415–447 (1992).

    Article  Google Scholar 

  18. Weinshall, D., Werman, M. & Tishby, N. Proc. 3rd Eur. Conf. Computer Vision (Springer, Stockholm, 1994).

    Google Scholar 

  19. Horn, B. K. P. & Schunk, B. G. Artif. Intel. 17, 185–203 (1981).

    Article  Google Scholar 

  20. Nakayama, K. & Silverman, G. H. Vision Res. 739–746 (1988).

  21. Cook, R. L. & Torrance, K. E. in SIGGRAPH-81 (Special Interest Group on Computer Graphics Conf. Proc. Assoc. for Computing Machinery, New York, 1981).

    Google Scholar 

  22. Freeman, W. T. in Proc. 4th Intl. Conf. Computer Vision 347–356 (IEEE, Berlin, Germany, 1993).

    Google Scholar 

  23. Bichsel, M. & Pentland, A. P. in Proc. IEEE Computer Vision and Pattern Recognition 459–465 (Champaign, IL, 1992).

    Google Scholar 

  24. Freeman, W. T. Intl. J. Comp. Vis. (in the press).

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Freeman, W. The generic viewpoint assumption in a framework for visual perception. Nature 368, 542–545 (1994).

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