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European Conference on the Applications of Evolutionary Computation

EvoApplications 2010: Applications of Evolutionary Computation pp 281–290Cite as

Refinement Techniques for Animated Evolutionary Photomosaics Using Limited Tile Collections

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6025)

Abstract

An animated evolutionary photomosaic is produced from a sequence of still or static photomosaics to evolve a near match to a given target image. A static photomosaic is composed of small digital images or tiles, each having their own aesthetic value. If the tiles are prepared manually, the tile collections are typically small. This potentially limits the visual quality of a photomosaic as there may not be sufficient options for matching tiles. We investigate the use of colour adjustment and tile size variation techniques via genetic programming to improve the animated photomosaics. The results show that colour adjustment improved both visual quality and fitness. However, it can produce strange looking tiles. Tile size variation was able to focus on details in the target image but produced slightly worse fitness values than an equal-sized tiles approach. Combining these techniques revealed that, regardless of the size of tiles, colour adjustment was the dominant refinement. In conclusion, each of these techniques is able to produce an aesthetically different animation effect and presents a better mechanism for generating photomosaics when only a limited number of tiles is available.

Keywords

  • Non-photorealistic rendering
  • animated evolved photomosaic
  • evolved art
  • genetic art
  • genetic programming
  • digital art

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  • DOI: 10.1007/978-3-642-12242-2_29
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Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001, Australia

    Shahrul Badariah Mat Sah, Vic Ciesielski & Daryl D’Souza

Authors
  1. Shahrul Badariah Mat Sah
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  2. Vic Ciesielski
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  3. Daryl D’Souza
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, Belfield, Dublin 4, UK

    Anthony Brabazon

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Wilhelm Schickard-Institut für Informatik, Universität Tübingen, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

  5. National University of Computer and Emerging Sciences (NUCES), A.K. Brohi Road, H-11/4, Islamabad, Pakistan

    Muddassar Farooq

  6. Fakultät für WiSo, HelmutSchmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  7. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  8. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

  10. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, UK

    Michael O’Neill

  11. Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Consiglio Nazionale delle Ricerche (CNR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

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Mat Sah, S.B., Ciesielski, V., D’Souza, D. (2010). Refinement Techniques for Animated Evolutionary Photomosaics Using Limited Tile Collections. In: , et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_29

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)