Advertisement

The Bayesian Draughtsman: A Model for Visuomotor Coordination in Drawing

  • Ruben Coen Cagli
  • Paolo Coraggio
  • Paolo Napoletano
  • Giuseppe Boccignone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

In this article we present a model of realistic drawing accounting for visuomotor coordination, namely the strategies adopted to coordinate the processes of eye and hand movement generation, during the drawing task. Starting from some background assumptions suggested by eye-tracking human subjects, we formulate a Bayesian model of drawing activity. The resulting graphical model is shaped in the form of a Dynamic Bayesian Network that combines features of both the Input–Output Hidden Markov Model and the Coupled Hidden Markov Model, and provides an interesting insight on mechanisms for dynamic integration of visual and proprioceptive information.

Keywords

Hide Markov Model Active Vision Dynamic Bayesian Network Proprioceptive Information Drawing Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zeki, S.: Inner Vision. An Exploration of Art and the Brain. Oxford University Press, Oxford, UK (1999)Google Scholar
  2. 2.
    Tchalenko, J., Dempere-Marco, R., Hu, X.P., Yang, G.Z.: Eye Movement and Voluntary Control in Portrait Drawing. In: The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research, ch. 33, Elsevier, Amsterdam (2003)Google Scholar
  3. 3.
    Coen Cagli, R., Coraggio, P., Napoletano, P.: DrawBot – A Bio–Inspired Robotic Portraitist. Digital Creativity Journal (in press, 2007)Google Scholar
  4. 4.
    Ramnani, N.: The primate cortico–cerebellar system: anatomy and function. Nature Review Neuroscience 7 (2006)Google Scholar
  5. 5.
    Todorov, E., Jordan, M.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002)CrossRefGoogle Scholar
  6. 6.
    Kording, K.P., Wolpert, D.M.: Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences 10(7) (2006)Google Scholar
  7. 7.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  8. 8.
    Pylyshyn, Z.W.: Situating vision in the world. Trends in Cognitive Sciences 4(5) (2000)Google Scholar
  9. 9.
    Land, M., Mennie, N., Rusted, J.: Eye movements and the roles of vision in activities of daily living: making a cup of tea. Perception 28, 1311–1328 (1999)CrossRefGoogle Scholar
  10. 10.
    Hayhoe, M.M., Ballard, D.H.: Eye Movements in Natural Behavior. Trends in Cognitive Science 9(188) (2005)Google Scholar
  11. 11.
    Goodale, M.A., Humphrey, G.K.: The objects of action and perception. Cognition 67, 181–207 (1998)CrossRefGoogle Scholar
  12. 12.
    Rizzolatti, G., Riggio, L., Sheliga, B.M.: Space and selective attention. In: Umiltà, C., Moscovitch, M. (eds.) Attention and Performance XV, MIT Press, Cambridge (1994)Google Scholar
  13. 13.
    Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. PhD dissertation, Berkeley, University of California, Computer Science Division (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ruben Coen Cagli
    • 1
  • Paolo Coraggio
    • 1
  • Paolo Napoletano
    • 2
  • Giuseppe Boccignone
    • 2
  1. 1.DSF, Robot Nursery Laboratory - Università di Napoli Federico II, via Cintia, NapoliItaly
  2. 2.Natural Computation Lab, DIIIE - Università di Salerno, via Ponte Don Melillo, 1 Fisciano (SA)Italy

Personalised recommendations