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A Stochastic Scale Space for Multiscale Image Representation

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Scale-Space Theories in Computer Vision (Scale-Space 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1682))

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Abstract

The notion of a stochastic scale space has been introduced through a stochastic approximation to the Perona-Malik equation. The approximate solution has been shown to preserve scale-space causality and is well-posed in an expected sense. The algorithm also converges to a (unique) constant image.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Science, Bangalore, 560 012, India

    Uma S. Ranjan & K.R. Ramakrishnan

Authors
  1. Uma S. Ranjan
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  2. K.R. Ramakrishnan
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Editor information

Editors and Affiliations

  1. The IT University in Copenhagen, Glentevej 67, DK-2400, Copenhagen NV, Denmark

    Mads Nielsen

  2. Department of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100, Copenhagen OE, Denmark

    Peter Johansen  & Ole Fogh Olsen  & 

  3. Department of Mathematics and Computer Science, University of Mannheim, D-68131, Mannheim, Germany

    Joachim Weickert

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© 1999 Springer-Verlag Berlin Heidelberg

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Ranjan, U.S., Ramakrishnan, K. (1999). A Stochastic Scale Space for Multiscale Image Representation. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_40

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  • DOI: https://doi.org/10.1007/3-540-48236-9_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66498-7

  • Online ISBN: 978-3-540-48236-9

  • eBook Packages: Springer Book Archive

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