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A Compact and Multiscale Image Model Based on Level Sets

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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

Multiscale segmentation respectful of the visual perception is an important issue of Computer Vision. We present an image model derived from the level sets representation which offers most of the prop- erties sought to a good segmentation: the borders are located at the per- ceptual edges; they are invariant by affine map and by contrast change; they are sorted according to their perceptual significance using a scale parameter. At last, a compact version of this model has been developed to be used in a progressive, and artifact-free, image compression scheme.

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Author information

Authors and Affiliations

  1. PRISME, UFR de Math. et Info., Univ. Paris 5 R.Descartes, 45 rue des Saints-Pères, 75270 Paris cedex 06, France

    Jacques Froment

  2. CMLA, Ecole Normale Supérieure de Cachan, 61 avenue du Président Wilson, 94235 Cachan cedex, France

    Jacques Froment

Authors
  1. Jacques Froment
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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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Froment, J. (1999). A Compact and Multiscale Image Model Based on Level Sets. 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_14

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  • DOI: https://doi.org/10.1007/3-540-48236-9_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66498-7

  • Online ISBN: 978-3-540-48236-9

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