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Nonlinear scale-space from n-dimensional sieves

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

Abstract

The one-dimensional image analysis method know as the sieve[1] is extended to any finite dimensional image. It preserves all the usual scale-space properties but has some additional features that, we believe, make it more attractive than the diffusion-based methods. We present some simple examples of how it might be used.

Keywords

  • Original Image
  • Mathematical Morphology
  • Connected Subset
  • Sequential Filter
  • Flat Zone

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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© 1996 Springer-Verlag Berlin Heidelberg

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Bangham, J.A., Harvey, R., Ling, P.D., Aldridge, R.V. (1996). Nonlinear scale-space from n-dimensional sieves. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015535

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  • DOI: https://doi.org/10.1007/BFb0015535

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