Neural Network Architecture for Modeling the Joint Visual Perception of Orientation, Motion, and Depth

  • Daniel Oberhoff
  • Andy Stynen
  • Marina Kolesnik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4021)


We present a methodology and a neural network architecture for the modeling of low- and mid-level visual processing. The network architecture uses local filter operators as basic processing units which can be combined into a network via flexible connections. Using this methodology we design a neuronal network that models the joint processing of oriented contrast changes, their motion and depth. The network reflects the structure and the functionality of visual pathways. We present network responses to a stereo video sequence, highlight the correspondence to biological counterparts, outline the limitations of the methodology, and discuss specific aspects of the processing and the extent of visual tasks that can be successfully carried out by the suggested neuronal architecture.


Simple Cell Neural Network Architecture Horizontal Edge Binocular Disparity Binocular Fusion 
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. [Adelson and Bergen, 1985]
    Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. 2, 284 (1985)CrossRefGoogle Scholar
  2. [Anzai et al., 2001]
    Anzai, A., Ohzawa, I., Freeman, R.D.: Joint-encoding of motion and depth by visual cortical neurons: neural basis of the pulfrich effect. Nature Neurosci. 4(5), 513–518 (2001)Google Scholar
  3. [Cao and Grossberg, 2004]
    Cao, Y., Grossberg, S.: A laminar cortical model of stereopsis and 3d surface perception: Closure and da vinci stereopsis. CAS/CNS Technical Report (2004)Google Scholar
  4. [Kolesnik and Barlit, 2003]
    Kolesnik, M., Barlit, A.: Iterative orientation tuning in v1: a simple cell circuit with cross-orientation suppression. LNCS (2003)Google Scholar
  5. [Koulakov and Chklovskii, 2001]
    Koulakov, A.A., Chklovskii, D.B.: Orientation preference patterns in mammalian visual cortex: A wire length minimization approach. Neuron 29, 519–527 (2001)CrossRefGoogle Scholar
  6. [Neumann and Mingolla, 2002]
    Neumann, H., Mingolla, E.: Contour and Surface Perception. In: Handbook of brain theory and neural networks. MIT Press, Cambridge (2002)Google Scholar
  7. [Neumann et al., 1999]
    Neumann, H., Pessoa, L., Hanse, T.: Interaction of on and off pathways for visual contrast measurement. Biol. Cybern. 81, 515–523 (1999)CrossRefGoogle Scholar
  8. [Ohzawa et al., 1996]
    Ohzawa, I., DeAngelis, G.C., Freeman, R.D.: Encoding of binocular disparity by simple cells in the cat’s visual cortex. J. Neurophys. 75(5), 1779–1805 (1996)Google Scholar
  9. [Perona and Malik., 1990]
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  10. [Sabatini and Solari, 2004]
    Sabatini, S.P., Solari, F.: Emergence of motion-in-depth selectivity in the visual cortex through linear combination of binocular energy complex cells with different ocular dominance. Neurocomp. 58-60, 865 (2004)CrossRefGoogle Scholar
  11. [Sabatini et al., 2003]
    Sabatini, S.P., Solari, F., Cavalleri, P., Bisio, G.: Phase-based binocular perception of motion in depth: Cortical-like operators and analog vlsi architectures. J. Appl. Sig. Proc. (2003)Google Scholar
  12. [Schwartz, 1977]
    Schwartz, E.L.: Spatial mapping in the primate sensory projection: analytic structure and relevance to perception. Biological Cybernetics 25(4), 181–194 (1977)CrossRefGoogle Scholar
  13. [Shmuel and Grinvald, 1996]
    Shmuel, A., Grinvald, A.: Functional organization for direction of motion and its relationship to orientation maps in cat area 18. J. Neurosci. 16(21), 6945 (1996)Google Scholar
  14. [Swindale, 1998]
    Swindale, N.V.: Cortical organization: Modules, polymaps and mosaics. Curr. Biol. 8 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Oberhoff
    • 1
  • Andy Stynen
    • 1
  • Marina Kolesnik
    • 1
  1. 1.Fraunhofer Institute for Applied Information Technology Schloss BirlinghovenSankt AugustinGermany

Personalised recommendations