Skip to main content
SpringerLink
Log in

Evaluating Motion Estimation Models from Behavioural and Psychophysical Data

  • Conference paper
Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010)

  • 1109 Accesses

Abstract

Offering proper evaluation methodology is essential to continue progress in modelling the neural mechanisms involved in vision information processing. Currently the evaluation of biologically inspired motion estimation models lacks a proper methodology for comparing their performance against behavioural and psychophysical data. Here we set the basis for such a new benchmark methodology based on human visual performance and designed a database of image sequences taken from neuroscience and psychophysics literature. In this article we focused on two fundamental aspects of motion estimation, which are the respective influence between 1d versus 2d cues and the dynamics of motion integration. Since motion models deal with many kinds of motion representations and scales, we defined two general readouts based on a global motion estimation. Such readouts, namely eye movements and perceived motion, will serve as a reference to compare simulated and experimental data. Baseline results are provided for biologically inspired artificial vision models but also for computer vision models. As a whole we provide here the basis for a valuable evaluation methodology to unravel the fundamental mechanisms of motion perception in the visual cortex. Our database is freely available on the web together with scoring instructions and results at: http://www-sop.inria.fr/neuromathcomp/psymotionbench

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

Access this chapter

Log in via an institution
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.

References

  1. Adelson, E.H., Bergen, J.R.: Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A 2(2), 284–299 (1985)

    Article  Google Scholar 

  2. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: International Conference on Computer Vision, ICCV 2007 (2007)

    Google Scholar 

  3. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. IJCV 12(1), 43–77 (1994)

    Article  Google Scholar 

  4. Barthelemy, F.V., Vanzetta, I., Masson, G.S.: Behavioral receptive field for ocular following in humans: dynamics of spatial summation and center-surround interactions. Journal of Neurophysiology 95(6), 3712 (2006)

    Article  Google Scholar 

  5. Bayerl, P., Neumann, H.: Disambiguating visual motion through contextual feedback modulation. Neural Computation 16(10), 2041–2066 (2004)

    Article  MATH  Google Scholar 

  6. Bayerl, P., Neumann, H.: Disambiguating visual motion by form-motion interaction–A computational model. International Journal of Computer Vision 72(1), 27–45 (2007)

    Article  Google Scholar 

  7. Beck, C., Neumann, H.: Interactions of motion and form in visual cortex – a neural model. Journal of Physiology - Paris 104, 61–70 (2010)

    Article  Google Scholar 

  8. Biber, U., Ilg, U.J.: Initiation of smooth-pursuit eye movements by real and illusory contours. Vision Research 48(8), 1002–1013 (2008)

    Article  Google Scholar 

  9. Born, R.T., Pack, C.C., Ponce, C., Yi, S.: Temporal evolution of 2-dimensional direction signals used to guide eye movements. Journal of Neurophysiology 95, 284–300 (2006)

    Article  Google Scholar 

  10. Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)

    Google Scholar 

  11. Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

    Google Scholar 

  12. Castet, E., Charton, V., Dufour, A.: The extrinsic/intrinsic classification of two-dimensional motion signals with barber-pole stimuli. Vision Research 39(5), 915–932 (1999)

    Article  Google Scholar 

  13. Cesmeli, E., Lindsey, D.T., Wang, D.L.: An oscillatory correlation model of human motion perception. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, vol. 4 (2000)

    Google Scholar 

  14. Giese, M.: Dynamic Neural Field Theory for Motion Perception. Springer (1998)

    Google Scholar 

  15. Grossberg, S., Mingolla, E., Pack, C.: A neural model of motion processing and visual navigation by cortical area MST. Cerebral Cortex 9(8), 878–895 (1999)

    Article  Google Scholar 

  16. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)

    Article  Google Scholar 

  17. Huang, X., Albright, T.D., Stoner, G.R.: Stimulus dependency and mechanisms of surround modulation in cortical area MT. Journal of Neuroscience 28(51), 13889 (2008)

    Article  Google Scholar 

  18. Hupé, J.M., Rubin, N.: The dynamics of bi-stable alternation in ambiguous motion displays: a fresh look at plaids. Vision Research 43(5), 531–548 (2003)

    Article  Google Scholar 

  19. Kooi, T.L.: Local direction of edge motion causes and abolishes the barberpole illusion. Vision Research 33(16), 2347–2351 (1993)

    Article  Google Scholar 

  20. Lorenceau, J., Shiffrar, M., Wells, N., Castet, E.: Different motion sensitive units are involved in recovering the direction of moving lines. Vision Research 33(9), 1207–1217 (1993)

    Article  Google Scholar 

  21. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop, pp. 121–130 (1981)

    Google Scholar 

  22. Masson, G.S., Ilg, U.J. (eds.): Dynamics of Visual Motion Processing Neuronal, Behavioral, and Computational Approaches, 1st edn. Springer (2010)

    Google Scholar 

  23. Masson, G.S., Rybarczyk, Y., Castet, E., Mestre, D.R.: Temporal dynamics of motion integration for the initiation of tracking eye movements at ultra-short latencies. Visual Neuroscience 17(05), 753–767 (2000)

    Article  Google Scholar 

  24. Masson, G.S., Stone, L.S.: From following edges to pursuing objects. Journal of Neurophysiology 88(5), 2869 (2002)

    Article  Google Scholar 

  25. Montagnini, A., Mamassian, P., Perrinet, L., Castet, E., Masson, G.S.: Bayesian modeling of dynamic motion integration. Journal of Physiology-Paris 101(1-3), 64–77 (2007)

    Article  Google Scholar 

  26. Pack, C.C., Born, R.T.: Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain. Nature 409, 1040–1042 (2001)

    Article  Google Scholar 

  27. Pack, C.C., Gartland, A.J., Born, R.T.: Integration of contour and terminator signals in visual area MT of alert macaque. The Journal of Neuroscience 24(13), 3268–3280 (2004)

    Article  Google Scholar 

  28. Pack, C.C., Hunter, J.N., Born, R.T.: Contrast dependence of suppressive influences in cortical area MT of alert macaque. Journal of Neurophysiology 93(3), 1809 (2005)

    Article  Google Scholar 

  29. Pack, C.C., Hunter, J.N., Born, R.T.: Contrast dependence of suppressive influences in cortical area MT of alert macaque. Journal of Neurophysiology 93(3), 1809–1815 (2005)

    Article  Google Scholar 

  30. Salgado, A., Sánchez, J.: Temporal Constraints in Large Optical Flow Estimation. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds.) EUROCAST 2007. LNCS, vol. 4739, pp. 709–716. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  31. Sceniak, M.P., Ringach, D.L., Hawken, M.J., Shapley, R.: Contrast’s effect on spatial summation by macaque V1 neurons. Nature Neuroscience 2(8), 733–739 (1999)

    Article  Google Scholar 

  32. Seriès, P., Georges, S., Lorenceau, J., Frégnac, Y.: A network view of the structure of center/surround modulations of V1 receptive field properties in visual and cortical spaces. Neurocomputing 38, 881–888 (2001)

    Article  Google Scholar 

  33. Strecha, C., Van Gool, L.: Motion - Stereo Integration for Depth Estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 170–185. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  34. Sun, D., Roth, S., Darmstadt, T.U., Black, M.J.: Secrets of Optical Flow Estimation and Their Principles. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  35. Tlapale, É., Masson, G.S., Kornprobst, P.: Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism. Vision Research 50(17), 1676–1692 (2010)

    Article  Google Scholar 

  36. Veltz, R., Faugeras, O.: Local/global analysis of the stationary solutions of some neural field equations. SIAM J. Applied Dynamical Systems 9(3), 954–998 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  37. Wallace, J.M., Stone, L.S., Masson, G.S.: Object motion computation for the initiation of smooth pursuit eye movements in humans. Journal of Neurophysiology 93(4), 2279–2293 (2005)

    Article  Google Scholar 

  38. Wallach, H.: Über visuell wahrgenommene Bewegungsrichtung. Psychological Research 20(1), 325–380 (1935)

    Article  Google Scholar 

  39. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 optical flow. In: Proceedings of the British Machine Vision Conference, BMVC (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Neuromathcomp Lab, INRIA Sophia Antipolis – Méditerranée, France

    Émilien Tlapale & Pierre Kornprobst

  2. Vision and Perception Science Lab, University of Ulm, Germany

    Jan D. Bouecke & Heiko Neumann

  3. DyVA Lab, INCM, UMR 6193 CNRS – Université de la Méditerranée, France

    Guillaume S. Masson

Authors
  1. Émilien Tlapale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pierre Kornprobst
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan D. Bouecke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heiko Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guillaume S. Masson
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 100 Morrissey Blvd, 02125, Boston, MA, USA

    Junichi Suzuki

  2. Graduate School of Engineering, 2-1 Yamada-oka, Suita, 565-0871, Osaka, Japan

    Tadashi Nakano

Rights and permissions

Reprints and permissions

Copyright information

© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Tlapale, É., Kornprobst, P., Bouecke, J.D., Neumann, H., Masson, G.S. (2012). Evaluating Motion Estimation Models from Behavioural and Psychophysical Data. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32615-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32614-1

  • Online ISBN: 978-3-642-32615-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation