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Patch-Based Mathematical Morphology for Image Processing, Segmentation and Classification

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9386))

Abstract

In this paper, a new formulation of patch-based adaptive mathematical morphology is addressed. In contrast to classical approaches, the shape of structuring elements is not modified but adaptivity is directly integrated into the definition of a patch-based complete lattice. The manifold of patches is learned with a nonlinear bijective mapping, interpreted in the form of a learned rank transformation together with an ordering of vectors. This ordering of patches relies on three steps: dictionary learning, manifold learning and out of sample extension. The performance of the approach is illustrated with innovative examples of patch-based image processing, segmentation and texture classification.

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Authors and Affiliations

  1. Normandie University, UNICAEN, ENSICAEN, GREYC UMR CNRS 6072, Caen, France

    Olivier Lézoray

Authors
  1. Olivier Lézoray
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Correspondence to Olivier Lézoray .

Editor information

Editors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università di Catania, Catania, Catania, Italy

    Sebastiano Battiato

  2. Arcueil CX, France

    Jacques Blanc-Talon

  3. Catania, Italy

    Giovanni Gallo

  4. Gent, Belgium

    Wilfried Philips

  5. CSIRO, Sydney, New South Wales, Australia

    Dan Popescu

  6. Vision Lab., University of Antwerp, Antwerpen, Belgium

    Paul Scheunders

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Lézoray, O. (2015). Patch-Based Mathematical Morphology for Image Processing, Segmentation and Classification. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-25903-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25902-4

  • Online ISBN: 978-3-319-25903-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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