Animating Typescript Using Aesthetically Evolved Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9596)

Abstract

The genotypic functions from apriori aesthetically evolved images are mutated progressively and their phenotypes sequenced temporally to produce animated versions. The animated versions are mapped onto typeface and combined spatially to produce animated typescript. The output is then discussed with reference to computer aided design and machine learning.

Keywords

Aesthetic evolution animated Animated typeface Typescript 

References

  1. 1.
    Sims, K.: Artificial evolution for computer graphics. Comput. Graph. 25(4), 319–328 (1991)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press, London (1992)MATHGoogle Scholar
  3. 3.
    Machado, P., Cardoso, A.: NEvAr - the assessment of an evolutionary art tool. In: Proceedings of the AISB 2000 Symposium on Creative and Cultural Aspects and Applications of AI and Cognitive Science, Birmingham, UK (2000)Google Scholar
  4. 4.
    Rooke, S.: Eons of Genetically Evolved Algorithmic Images. Morgan Kaufmann Publishers Inc., San Francisco (2002)CrossRefGoogle Scholar
  5. 5.
    McCormack, J.: Aesthetic evolution of l-systems revisited. In: EvoWorkshops, pp. 477–488 (2004)Google Scholar
  6. 6.
    Mills, A.: Evolving aesthetic images. MSc Mini Project Thesis (2005). https://www.ashleymills.com/ae/EvolutionaryArt.pdf
  7. 7.
    Martins, T., Correia, J., Costa, E., Machado, P.: Evotype: evolutionary type design. In: Johnson, C., Carballal, A., Correia, J. (eds.) EvoMUSART 2015. LNCS, vol. 9027, pp. 136–147. Springer, Heidelberg (2015)Google Scholar
  8. 8.
  9. 9.
    Merritt, L., Vanam, R.: x264: A high performance h. 264/AVC encoder (2006). http://neuron2.net/library/avc/overview_x264_v8_5.pdf
  10. 10.
    Noé, A.: Matroska file format (2009). http://www.matroska.org/files/matroska.pdf
  11. 11.
    Multimedia examples of the artifacts described in this paper. http://www.evoart.club/evomusart2016
  12. 12.
    Nikolov, N.: Organo font landing page. http://logomagazin.com/organo-font/
  13. 13.
    Cover, T.: Geometrical and statistical properties of systems of linear in equalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 3(EC–14), 326–334 (1965)CrossRefMATHGoogle Scholar
  14. 14.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  15. 15.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)CrossRefMATHGoogle Scholar
  16. 16.
    Wolf, A., Swift, J.B., Swinney, H.L., Vastano, J.A.: Determining Lyapunov exponents from a time series. Physica D 16(3), 285–317 (1985)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of ComputingThe University of KentCanterburyUK

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