Object Detection in Natural Scenes by Feedback

  • Fred H. Hamker
  • James Worcester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)


Current models of object recognition generally assume a bottom-up process within a hierarchy of stages. As an alternative, we present a top-down modulation of the processed stimulus information to allow a goal-directed detection of objects within natural scenes. Our procedure has its origin in current findings of research in attention which suggest that feedback enhances cells in a feature-specific manner. We show that feedback allows discrimination of a target object by allocation of attentional resources.


Object Recognition Object Detection Spatial Attention Natural Scene Target Template 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mumford D.: On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol. Cybern. 66 (1992) 241–251.CrossRefGoogle Scholar
  2. 2.
    Tononi, G., Sporns, O., Edelman, G.: Reentry and the problem of integrating multiple cortical areas: Simulation of dynamic integration in the visual system. Cereb. Cortex 2 (1992) 310–335.CrossRefGoogle Scholar
  3. 3.
    Grossberg S. How does a brain build a cognitive code? Psychol Rev. 87 (1980) 1–51.CrossRefGoogle Scholar
  4. 4.
    Ullman, S.: Sequence seeking and counter streams: Acomputational model for bidirectional flow in the visual cortex. Cerebral Cortex 5 (1995) 1–11.CrossRefGoogle Scholar
  5. 5.
    Hamker, F.H.: The role of feedback connections in task-driven visual search. In: Connectionist Models in Cognitive Neuroscience. Springer Verlag, London (1999), 252–261.CrossRefGoogle Scholar
  6. 6.
    Corchs, S., Deco, G.: Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data. Cereb. Cortex 12 (2002) 339–348.CrossRefGoogle Scholar
  7. 7.
    Hamker, F.H.: How does the ventral pathway contribute to spatial attention and the planning of eye movements? Proceedings of the 4th Workshop Dynamic Perception, 14-15 November 2002, Bochum, Germany, to appear.Google Scholar
  8. 8.
    Desimone, R., Duncan, J., Neural mechanisms of selective attention. Annu Rev Neurosci 18 (1995) 193–222.CrossRefGoogle Scholar
  9. 9.
    Chelazzi, L., Duncan, J., Miller, E. K., Desimone, R.: Responses of neurons in inferior temporal cortex during memory-guided visual search. J. Neurophysiol. 80 (1998) 2918–2940.Google Scholar
  10. 10.
    Motter, B.C.: Neural correlates of attentive selection for color or luminance in extrastriate area V4. J. Neurosci. 14 (1994) 2178–2189.Google Scholar
  11. 11.
    Treue, S., Mart’inez Trujillo, J.C.: Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399 (1999) 575–579.CrossRefGoogle Scholar
  12. 12.
    Li, F.-F., VanRullen, R., Koch, C., Perona, P.: Rapid natural scene categorization in the near absence of attention. Proc Natl Acad Sci USA 99 (2002) 9596–9601.Google Scholar
  13. 13.
    Sheinberg, D.L., Logothetis, N.K.: Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision. J Neurosci. 21 (2001) 1340–1350.Google Scholar
  14. 14.
    Itti, L., Koch, C., Niebur, E.: Amo del of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 20 (1998), 1254–1259.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Fred H. Hamker
    • 1
  • James Worcester
    • 1
  1. 1.Division of BiologyCalifornia Institute of TechnologyPasadenaUSA

Personalised recommendations