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Attribute Controlled Reconstruction and Adaptive Mathematical Morphology

  • Conference paper
Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7883))

Abstract

In this paper we present a reconstruction method controlled by the evolution of attributes. The process begins from a marker, propagated over increasing quasi–flat zones. The evolution of several increasing and non–increasing attributes is studied in order to select the appropriate region. Additionally, the combination of attributes can be used in a straightforward way.

To demonstrate the performance of our method, three applications are presented. Firstly, our method successfully segments connected objects in range images. Secondly, input–adaptive structuring elements (SE) are defined computing the controlled propagation for each pixel on a pilot image. Finally, input–adaptive SE are used to assess shape features on the image.

Our approach is multi–scale and auto–dual. Compared with other methods, it is based on a given attribute but does not require a size parameter in order to determine appropriate regions. It is useful to extract objects of a given shape. Additionally, our reconstruction is a connected operator since quasi–flat zones do not create new contours on the image.

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Authors and Affiliations

  1. CMM - Centre de Morphologie Mathématique, Mathématiques et Systèmes, Mines ParisTech, 35 rue St Honoré, 77305, Fontainebleau–Cedex, France

    Andrés Serna & Beatriz Marcotegui

Authors
  1. Andrés Serna
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  2. Beatriz Marcotegui
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Editor information

Editors and Affiliations

  1. Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Box 337, 751 05, Uppsala, Sweden

    Cris L. Luengo Hendriks , Gunilla Borgefors  & Robin Strand ,  & 

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Serna, A., Marcotegui, B. (2013). Attribute Controlled Reconstruction and Adaptive Mathematical Morphology. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2013. Lecture Notes in Computer Science, vol 7883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38294-9_18

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  • DOI: https://doi.org/10.1007/978-3-642-38294-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38293-2

  • Online ISBN: 978-3-642-38294-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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