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Optimal Patch Assignment for Statistically Constrained Texture Synthesis

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Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

This article introduces a new model for patch-based texture synthesis that controls the distribution of patches in the synthesized texture. The proposed approach relies on an optimal assignment of patches over decimated pixel grids. This assignment problem formulates the synthesis as the minimization of a discrepancy measure between input’s and output’s patches through their optimal permutation. The resulting non-convex optimization problem is addressed with an iterative algorithm alternating between a patch assignment step and a patch aggregation step. We show that this model statistically constrains the output texture content, while inheriting the structure-preserving property of patch-based methods. We also propose a relaxed patch assignment extension that increases the robustness to non-stationnary textures.

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Authors and Affiliations

  1. Polytechnique Montréal, Montréal, Canada

    Jorge Gutierrez & Thomas Hurtut

  2. Normandie Univ., ENSICAEN, CNRS, GREYC, Caen, France

    Julien Rabin

  3. Laboratoire MAP5, Université Paris Descartes and CNRS, Sorbonne Paris Cité, Paris, France

    Bruno Galerne

Authors
  1. Jorge Gutierrez
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  2. Julien Rabin
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  3. Bruno Galerne
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  4. Thomas Hurtut
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Corresponding author

Correspondence to Jorge Gutierrez .

Editor information

Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Gutierrez, J., Rabin, J., Galerne, B., Hurtut, T. (2017). Optimal Patch Assignment for Statistically Constrained Texture Synthesis. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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