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Shape, Contour and Grouping in Computer Vision pp 9–21Cite as

An Empirical-Statistical Agenda for Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1681))

Abstract

This piece first describes what I see as the significant weaknesses in current un- derstanding of object recognition. We lack good schemes for: using unreliable information — like radiometric measurements — effectively; integrating poten- tially contradictory cues; revising hypotheses in the presence of new information; determining potential representations from data; and suppressing individual dif- ferences to obtain abstract classes. The problems are difficult, but none are unapproachable, given a change of emphasis in our research.

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References

  1. Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. IEEE T. Pattern Analysis and Machine Intelligence, 19(11):1300–1305, 1997.

    Article  Google Scholar 

  2. T.O. Binford. Visual perception by computer. In Proc. IEEE Conference on Systems and Control, 1971.

    Google Scholar 

  3. G. Buchsbaum. A spatial processor model for object colour perception. J. Franklin Inst., 310:1–26, 1980.

    Article  Google Scholar 

  4. G. Finlayson. Colour in perspective. IEEE T. Pattern Analysis and Machine Intelligence, 18:1034–1038, 1996.

    Article  Google Scholar 

  5. D.A. Forsyth. A novel algorithm for colour constancy. Int. J. Computer Vision, 5:5–36, 1990.

    Article  Google Scholar 

  6. D.A. Forsyth Sampling, Resampling and Colour Constancy. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1991.

    Google Scholar 

  7. P.J. Green. Reversible jump markov chain monte carlo computation and bayesian model determination. Biometrika, 82(4):711–732, 1995.

    Article  MathSciNet  Google Scholar 

  8. H. Helson. Some factors and implications of colour constancy. J. Opt. Soc. America, 48:555–567, 1934.

    Google Scholar 

  9. C-Y. Huang, O.T. Camps, and T. Kanungo. Object recognition using appearance-based parts and relations. In IEEE Conf. on Computer Vision and Pattern Recog-nition, pages 877–83, 1997.

    Google Scholar 

  10. Mark Jerrum and Alistair Sinclair. The markov chain monte carlo method: an approach to approximate counting and integration. In D.S. Hochbaum, editor, Approximation Algorithms for NP-hard Problems. PWS Publishing, Boston, 1996.

    Google Scholar 

  11. D.B. Judd. Hue, saturation and lightness of surface colors with chromatic illumination. J. Opt. Soc. America, 30:2–32, 1940.

    Article  Google Scholar 

  12. G.J. Klinker, S.A. Shafer, and T. Kanade. A physical approach to color image understanding. Int. J. Computer Vision, 4(1):7–38, 1994.

    Article  Google Scholar 

  13. E.H. Land and J.J. McCann. Lightness and retinex theory. J. Opt. Soc. Am., 61(1):1–11, 1971.

    Article  Google Scholar 

  14. H.C. Lee. Method for computing the scene-illuminant chromaticity from specular highlights. J. Opt. Soc. Am.-A, 3:1694–1699, 1986.

    Article  Google Scholar 

  15. L.T. Maloney and B.A. Wandell. A computational model of color constancy. J. Opt. Soc. Am, 1:29–33, 1986.

    Article  Google Scholar 

  16. D.H. Marimont and B.A. Wandell. Linear models of surface and illuminant spectra. J. Opt. Soc. Am.-A, 9:1905–1913, 1992.

    Article  Google Scholar 

  17. D. J. Murdoch and P. J. Green. Exact sampling from a continuous state space. To appear in the Scandinavian Journal of Statistics, 1998.

    Google Scholar 

  18. R.M. Neal. Probabilistic inference using markov chain monte carlo methods. Computer science tech report crg-tr-93-1, University of Toronto, 1993.

    Google Scholar 

  19. James G. Propp and David B. Wilson. Exact sampling with coupled markov chains and applications to statistical mechanics. Random Structures and Algorithms, 9:223–252, 1996.

    Article  MathSciNet  Google Scholar 

  20. E. Rosch and C.B. Mervis. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7:573–605, 1975.

    Article  Google Scholar 

  21. E. Rosch, C.B. Mervis, W.E. Gray, D.M. Johnson, and P. Boyes-Braim. Basic objects in natural categories. Cognitive Psychology, 8:382–439, 1976.

    Article  Google Scholar 

  22. A. Zisserman, D.A. Forsyth, J.L. Mundy, C.A. Rothwell, and J.S. Liu. 3d object recognition using invariance. Articial Intelligence, 78:239–288, 1995.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Division, U.C. Berkeley, Berkeley, CA, 94720, USA

    David Forsyth

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  1. David Forsyth
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© 1999 Springer-Verlag Berlin Heidelberg

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Forsyth, D. (1999). An Empirical-Statistical Agenda for Recognition. In: Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science, vol 1681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46805-6_2

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  • DOI: https://doi.org/10.1007/3-540-46805-6_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66722-3

  • Online ISBN: 978-3-540-46805-9

  • eBook Packages: Springer Book Archive

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