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Optimization Techniques on Pixel Neighborhood Graphs for Image Processing

  • Conference paper
Graph Based Representations in Pattern Recognition

Part of the book series: Computing Supplement ((COMPUTING,volume 12))

Abstract

A class of image processing problems is considered from the standpoint of treating them as those of co-ordinating the local image-dependent information and a priori smoothness constraints. Such a generalized problem is set as the formal problem of minimization of a separable objective function defined on an appropriate pixel neighborhood graph. For attaining a higher computation speed, the full pixel lattice is replaced by a succession of partial identical neighborhood trees. Two versions of a high-speed minimization procedure are proposed for, respectively, discretely defined and quadratic objective functions.

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References

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Author information

Authors and Affiliations

  1. Tula State University, Lenin Ave. 92, 300600, Tula, Russia

    V. V. Mottl, A. B. Blinov, A. V. Kopylov & A. A. Kostin

Authors
  1. V. V. Mottl
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  2. A. B. Blinov
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  3. A. V. Kopylov
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  4. A. A. Kostin
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Editor information

Editors and Affiliations

  1. Laboratoire Reconnaisance de Formes et Vision, INSA, Lyon, France

    Jean-Michel Jolion

  2. Pattern Recognition and Image Processing Group, Institute of Automation, University of Vienna, Austria

    Walter G. Kropatsch

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© 1998 Springer-Verlag Wien

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Mottl, V.V., Blinov, A.B., Kopylov, A.V., Kostin, A.A. (1998). Optimization Techniques on Pixel Neighborhood Graphs for Image Processing. In: Jolion, JM., Kropatsch, W.G. (eds) Graph Based Representations in Pattern Recognition. Computing Supplement, vol 12. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6487-7_14

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  • DOI: https://doi.org/10.1007/978-3-7091-6487-7_14

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83121-2

  • Online ISBN: 978-3-7091-6487-7

  • eBook Packages: Springer Book Archive

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