Neural Model for the Influence of Shading on the Multistability of the Perception of Body Motion

Leonid Fedorov, Joris Vangeneugden, Martin Giese

Abstract

Body motion perception from impoverished stimuli shows interesting dynamic properties, such as multistability and spontaneous perceptual switching. Psychophysical experiments show that such multistability disappears when the stimulus includes also shading cues along the body surface. Classical neural models for body motion perception have not addressed perceptual multistability. We present an extension of a classical neurodynamic model for biological and body motion perception that accounts for perceptual switching, and its dependence on shading cues on the body surface. We demonstrate that a set of psychophysical observations can be accounted for in a unifying manner by a hierarchical neural model for body motion processing that includes an additional shading pathway, which processes luminance gradients within the individual body segments. The goal of our model is to explain psychophysics and neural mechanism in the brain.

References

  1. Amari, S. (1977). Dynamics of pattern formation in lateral inhibition type neural fields. Biological Cybernetics.
  2. Andersen, R. and Bradley, D. (1998). Perception of threedimensional structure from motion. Trends in Cognitive Sciences.
  3. Barraclough, N. and Jellema, T. (2011). Visual aftereffects for walking actions reveal underlying neural mechanisms for action recognition. Psychological Science.
  4. Blake, R. and Logothesis, N. (2001). Visual competition. Nature Review Neuroscience.
  5. de la Rosa, S., Streuber, S., Giese, M., Buelthoff, H., and Curio, C. (2014). Putting actions in context: Visual action adaptation aftereffects are modulated by social contexts. PLOS One.
  6. Edwards, M., Deng, J., and Xie, X. (2016). From pose to activity. In Computer Vision and Image Understanding. Elsevier Science Inc.
  7. Escobar, M. and Kornprobst, P. (2008). Action recognition with a bioinspired feedforward motion processing model: the richness of center-surround interactions. In ECCV'08. 10th European Conference on Computer Vision. Springer Berlin Heidelberg.
  8. Giese, M. (2014). Skeleton model for the neurodynamics of visual action representations. In Artificial Neural Networks and Machine Learning ICANN 2014, Lecture Notes in Computer Science. Springer International Publishing.
  9. Giese, M. and Poggio, T. (2003). Neural mechanisms for the recognition of biological movements and action. Nature Reviews Neuroscience.
  10. Hock, H., Kelso, J., and Schoener, G. (1993). Bistability and hysteresis in the perceptual organization of apparent motion. Journal of Experimental Psychology: Human Perception and Performance.
  11. Jackson, S. and Blake, R. (2010). Neural integration of information specifying human structure from form, motion, and depth. Journal of Neuroscience.
  12. Jhuang, H., Serre, T., Wolf, L., and Poggio, T. (2007). A biologically inspired system for action recognition. In 2007 IEEE 11th International Conference on Computer Vision. IEEE.
  13. Lange, J. and Lappe, M. (2006). A model for biological motion perception from configural form cues. Journal of Neuroscience.
  14. Layher, G., Giese, M., and Neumann, H. (2014). Learning representations of animated motion sequencesa neural model. In Topics in Cognitive Science. Topics in Cognitive Science.
  15. Lee, T., Belkhatir, M., and Sanei, S. (2014). A comprehensive review of past and present vision-based techniques for gait recognition. In Multimedia Tools and Applications. Kluwer Academic Publishers.
  16. Leopold, D. and Logothetis, N. (1999). Multistable phenomena: changing views in perception. Trends in Cognitive Science.
  17. Nguyen, D., Li, W., and Ogunbona, P. (2016). Human detection from images and videos. In Pattern Recognition. Elsevier Science Inc.
  18. Pastukhov, A., Garca-Rodrguez, P., Haenicke, J., Guillamon, A., Deco, G., and Braun, J. (2013). Multi-stable perception balances stability and sensitivity. Frontiers in Computational Neuroscience.
  19. Rankin, J., Meso, A., Masson, G. S., Faugeras, O., and Kornprobst, P. (2014). Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration. Journal of Computational Neuroscience.
  20. Sterzer, P., Kleinschmidt, A., and Rees, G. (2009). The neural bases of multistable perception. Trends in Cognitive Science.
  21. Thurman, S. and Lu, H. (2014). Bayesian integration of position and orientation cues in perception of biological and non-biological forms. Frontiers in Human Neuroscience.
  22. Thurman, S. and Lu, H. (2016). A comparison of form processing involved in the perception of biological and nonbiological movements. Journal of Vision.
  23. Tyler, C. (2011). Computer Vision: From Surfaces to 3D Objects. Chapman & Hall/CRC, London, 1st edition.
  24. Vangeneugden, J., de Maziere, P., van Hulle, M., Jaeggli, T., van Gool, L., and Vogels, R. (2011). Distinct mechanisms for coding of visual actions in macaque temporal cortex. Journal of Neuroscience.
  25. Vangeneugden, J., van Ee, R., Verfaillie, K., Wagemans, J., and de Beeck, H. (2012). Activity in areas mt+ and eba, but not psts, allow prediction of perceptual states during ambiguous biological motion. In Society for Neuroscience Meeting. Society for Neuroscience.
  26. Vanrie, J. and Verfaillie, K. (2004). Perception of biological motion: A stimulus set of human point-light actions. Behavior Research Methods, Instruments, and Computers.
  27. Vanrie, J. and Verfaillie, K. (2006). Perceiving depth in point-light actions. Perception and Psychophysics.
  28. Wilson, H. (2003). Computational evidence for a rivalry hierarchy in vision. Proceedings of the National Academy of Sciences.
  29. Ziaeefard, M. and Bergevin, R. (2015). Semantic human activity recognition: A literature review. In Pattern Recognition. Elsevier Science Inc.
Download


Paper Citation


in Harvard Style

Fedorov L., Vangeneugden J. and Giese M. (2016). Neural Model for the Influence of Shading on the Multistability of the Perception of Body Motion . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 69-76. DOI: 10.5220/0006054000690076


in Bibtex Style

@conference{ncta16,
author={Leonid Fedorov and Joris Vangeneugden and Martin Giese},
title={Neural Model for the Influence of Shading on the Multistability of the Perception of Body Motion},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)},
year={2016},
pages={69-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006054000690076},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)
TI - Neural Model for the Influence of Shading on the Multistability of the Perception of Body Motion
SN - 978-989-758-201-1
AU - Fedorov L.
AU - Vangeneugden J.
AU - Giese M.
PY - 2016
SP - 69
EP - 76
DO - 10.5220/0006054000690076