Skip to main content

TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling

  • Conference paper
Attention and Performance in Computational Vision (WAPCV 2004)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3368))

Abstract

A number of computational models of visual attention exist, but making comparisons is difficult due to the incompatible implementations and levels at which the simulations are conducted. To address this issue, we have developed a general-purpose neural network simulator that allows all of these models to be implemented in a unified framework. The simulator allows for the distributed execution of models, in a heterogeneous environment. Graphical tools are provided for the development of models by non-programmers and a common model description format facilitates the exchange of models. In this paper we will present the design of the simulator and results that demonstrate its generality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wilson, H.: Non-fourier cortical processes in texture, form and motion perception. In: Ulinski, et al. (eds.) Cerebral Cortex, vol. 13. Kluwer Academic/Plenum Publishers, Dordrecht (1999)

    Google Scholar 

  2. Itti, L., Koch, C.: Computational modeling of visual attention. Nature Reviews Neuroscience 2, 194–203 (2001)

    Article  Google Scholar 

  3. McCulloch, W.S., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  4. Tsotsos, J.K., Culhane, S., Wai, W., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artifical Intelligence 78, 507–547 (1995)

    Article  Google Scholar 

  5. Itti, L., Koch, C., Nierbur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)

    Article  Google Scholar 

  6. Reynolds, J.H., Chelazzi, L., Desimone, R.: Competitive mechanisms subserve attention in macaque areas v2 and v4. The Journal of Neuroscience 19, 1736–1753 (1999)

    Google Scholar 

  7. Rolls, E.T., Deco, G.: Computational Neuroscience of Vision. Oxford Univ. Press, Oxford (2002)

    Google Scholar 

  8. Lee, K.W., Buxton, H., Feng, J.F.: Selective attention for cue-guided search using a spiking neural network. In: Paletta, L., Humphreys, G.W., Fisher, R.B. (eds.) Proc. International Workshop on Attention and Performance in Computer Vision, pp. 55–63 (2003)

    Google Scholar 

  9. Wilson, H.R.: Spikes, decisions and actions: dynamical foundations of neuroscience. Oxford University Press, Oxford (1999)

    MATH  Google Scholar 

  10. Alexandrescu, A.: Modern C++ Design. Addison-Wesley, Reading (2001)

    Google Scholar 

  11. Glass, L.: Moire effect from random dots. Nature 223, 578–580 (1969)

    Article  Google Scholar 

  12. Zaharescu, A., Rothenstein, A.L., Tsotsos, J.K.: Towards a biologically plausible active visual search model. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G.W. (eds.) WAPCV 2004. LNCS, vol. 3368, pp. 67–74. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  13. Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J., Pomplun, M., Simine, E., Zhou, K.: Attending to motion: Localizing and classifying motion patterns in image sequence. In: Bülthoff, H.H., Lee, S.W., Poggio, T., Wallraven, C. (eds.) Proceedings of the Second International Workshop on Biologically Motivated Computer Vision, Tübingen, Germany, pp. 439–452 (2004)

    Google Scholar 

  14. Stuttgart neural network simulator, http://www-ra.informatik.uni-tuebingen.de/snns/

  15. Genesis, http://www.genesis-sim.org/genesis/

  16. ilab neuromorphic vision c++ toolkit, http://ilab.usc.edu/toolkit/

  17. Amygdala, http://amygdala.sourceforge.net/

  18. PDP++, http://www.cnbc.cmu.edu/resources/pdp++

  19. Neuralworks, http://www.neuralware.com/

  20. Neural network toolbox for matlab, http://www.mathworks.com/products/neuralnet/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rothenstein, A.L., Zaharescu, A., Tsotsos, J.K. (2005). TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30572-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24421-9

  • Online ISBN: 978-3-540-30572-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics