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Shape from appearance: A statistical approach to surface shape estimation

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1064)


This paper is concerned with surface shape estimation by a method in which an empirically determined associative model relating appearance to surface shape is used. Significantly, the estimated model is more accurate than the algorithm that generates the examples. The method presented here is a generalization of shape from shading methods that does not rely upon idealized models of the image formation process. As a relative of shape from shading, this method more accurately recovers small surface detail than is possible with methods such as stereo and motion. The present approach is a continuous analogue of pattern recognition and is closely related to methods of joint space learning used in robotics. Experiments on real scenes are used to illustrate the concepts involved.


  • Surface Shape
  • Conditional Density
  • Level Curf
  • Real Scene
  • Associative Model

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.

This research was supported by the Advanced Research Projects Agency and the National Science Foundation under grant IRI-89-02728 and by the Army Advance Construction Technology Center under grant DAAL 03-87-K-0006.


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© 1996 Springer-Verlag Berlin Heidelberg

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Hougen, D.R., Ahuja, N. (1996). Shape from appearance: A statistical approach to surface shape estimation. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61122-6

  • Online ISBN: 978-3-540-49949-7

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