Journal of Mathematical Imaging and Vision

, Volume 53, Issue 3, pp 332–345 | Cite as

Exploring the Space of Abstract Textures by Principles and Random Sampling

  • Luis Alvarez
  • Yann Gousseau
  • Jean-Michel Morel
  • Agustín Salgado
Article
First Online:
Received:
Accepted:

Abstract

Exemplar-based texture synthesis methods try to emulate textures observed in our visual world. Yet the field of all possible textures (natural or not) has been little explored. Indeed, existing abstract synthesis methods focus on a single generation rule and generate a rather limited set of textures. This limitation can be overcome by combining randomly various generation principles and rule parameters. Doing so gives access to a vast and still unexplored set of possible images. In this paper, we introduce an image sampling method combining the main painting techniques of abstract art. This sampler synthesizes what we call multi-layered textures. The underlying image model extends three abstract image synthesis models: the dead leaves model, the spot noise, and fractal generators. By respecting minimal self-similarity rules keeping Gestalt theory grouping principles at each texture layer, the abstract textures remain understandable to human perception. The complexity of the generated textures derives from the systematic and randomized use of shape interaction principles taken from abstract art such as occlusion, transparency, exclusion, inclusion, and tessellation.

Keywords

Texture synthesis Abstract painting Graphic design Dead leaves model Gestalt theory 
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Notes

Acknowledgments

Work partially supported by ERC advanced Grant Twelve Labours, and ONR Grant N00014-97-1-0839 (J.-M.M.).

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Luis Alvarez
    • 1
  • Yann Gousseau
    • 2
  • Jean-Michel Morel
    • 3
  • Agustín Salgado
    • 1
  1. 1.CTIM, Departamento de Informática y SistemasUniversidad de Las Palmas de Gran CanariaLas PalmasSpain
  2. 2.Telecom Paristech - LTCI CNRSParisFrance
  3. 3.Centre de Mathématiques et de Leurs Applications (CMLA)Ecole Normale Supérieure de CachanCachanFrance

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