Cognitive Computation

, Volume 5, Issue 4, pp 589–609 | Cite as

Binocular Energy Estimation Based on Properties of the Human Visual System



3D applications are very popular nowadays. They allow to bring new sensations (e.g., cinema, gaming, etc) and new ways for analyzing data (e.g., video-surveillance, pattern recognition, etc). The large availability of 3D data ia better understanding of human 3D perception in order to improve the quality of 3D visual data, increase the visual comfort and avoid visual fatigue and visual illness. In this paper, we explore the binocular perception through its various indices. The focus is put on the binocular energy and its evolution with regard to image impairments. Two types of cells are explored, that is, simple and complex cells responsible of the sensory fusion in the visual cortex. A model is proposed for these cells in order to estimate the binocular energy from color stereo-pairs. The integration of stereoscopic constraints such as unicity, coherence and occlusion allows to refine the proposed model described previously by taking into account the occluded and the non-occluded information. A deep experimentation demonstrates the efficiency of the described modeling. The estimated binocular energy presents a correlation with the impairment level caused by compression or noise.


  1. 1.
    Adelson EH, Bergen JR. Spatiotemporal energy models for the perception of motion. J Opt Soc Am. 1985;2(2):284–99.CrossRefGoogle Scholar
  2. 2.
    Akhter R, Sazzad ZMP, Horita Y, Baltes J. No reference stereoscopic image quality assessment. Image Qual Syst Perform. 2010;7524:17–21.Google Scholar
  3. 3.
    Anderson BL. The role of partial occlusion in stereopsis. Nature. 1994;367(6461):365–68.PubMedCrossRefGoogle Scholar
  4. 4.
    Ates HF, Orchard MT. A nonlinear image representation in wavelet domain using complex signals with single quadrant spectrum. In: Asilomar conference signals, systems, computers, vol 2. 2003;pp. 1966–70.Google Scholar
  5. 5.
    Barlow HB, Blakemore C, Pettigrew JD. The neural mechanism of binocular depth discrimination. J Physiol. 1967;193:327–42PubMedGoogle Scholar
  6. 6.
    Blake R, Wilson HR. Neural models of stereoscopic vision. Trends in neurosciences. Int J Comput Vis. 1991;14:445–52.Google Scholar
  7. 7.
    Campbell FW, Cooper GF, Enroth-Cugell C. The spatial selectivity of the visual cells of the cat. J Physiol. 1969;203:223–35.Google Scholar
  8. 8.
    Candès E, Demanet L, Donoho D, Ying L. Fast discrete curvelet transforms. Multiscale Model Simul. 2005;5(3):861–99.Google Scholar
  9. 9.
    Chessa M, Canessa A, Gibaldi A, Solari F, Sabatini S. Embedding fixation constraints into binocular energy-based models of depth perception. In: International conference on cognitive and neural systems. Boston, Massachusetts; 2009.Google Scholar
  10. 10.
    Christen WG, Mower DG. Effects of monocular occlusion and diffusion on visual system development in the cat. Brain Res. 1987;415(2):233–41.PubMedCrossRefGoogle Scholar
  11. 11.
    DeAngelis GC, Ohzawa I, Freeman RD. Depth is encoded in the visual cortex by a specialized receptive field structure. J Nat. 1991;352:156–59.CrossRefGoogle Scholar
  12. 12.
    Delorme A, Fluckiger M. Perception et réalité, une introduction à la psychologie des perceptions. Brussels: De Boeck; 2003.Google Scholar
  13. 13.
    Do M, Vetterli M. Pyramidal directional filter banks and curvelets. In: IEEE proceedings of the international conference image; 2001.Google Scholar
  14. 14.
    Donoho DL. Wedgelets: nearly-minimax estimation of edges, annals of stat. Commun Pure Appl Math. 1999;27(3):859–97.Google Scholar
  15. 15.
    Fleet DJ, Wagner H, Heeger DJ. Disparity from local weighted phase-correlation. In: Proceedings of the IEEE international conference on systems. Man, Cybernetics; 1994. p. 48–56.Google Scholar
  16. 16.
    Fleet DJ, Wagner H, Heeger DJ. Neural encoding of binocular disparity: energy model, position shifts and phase shifts. Vis Res. 1996;36(12):1839–857.PubMedCrossRefGoogle Scholar
  17. 17.
    Foster KH, Gaska JP, Marcelja S, Pollen DA. Phase relationships between adjacent simple cells in the feline visual cortex. J Physiol. 1983;345:22.Google Scholar
  18. 18.
    Geiger D, Ladendorf B, Yuille A. Occlusions and binocular stereo. Int J Comput Vis. 1995;14:211–26.CrossRefGoogle Scholar
  19. 19.
    Geman S, Geman D. Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 1984;6(6):721–41.PubMedCrossRefGoogle Scholar
  20. 20.
    Grossberg S. 3d vision and figure ground separation by visual cortex. Percept Psychophys. 1994;55(1): 48–120.PubMedCrossRefGoogle Scholar
  21. 21.
    Grossberg S, McLoughlin PN. Cortical dynamics of three-dimensional surface perception: binocular and half-occluded scenic images. Trans Soc Comput Simul Int. 1997;14:583–1605.Google Scholar
  22. 22.
    Hayashi R, Maeda T, Shimojo S, Tachi S. An integrative model of binocular vision: a stereo model utilizing interocularly unpaired points produces both depth and binocular rivalry. Vis Res. 2004;44(20):2367–380.PubMedCrossRefGoogle Scholar
  23. 23.
    Haynes JD, Deichmann R, Rees G. Eye-specific effects of binocular rivalry in the human lateral geniculate nucleus. J Nat. 2005;438(7067):496–99.CrossRefGoogle Scholar
  24. 24.
    Hibbard PB. Binocular energy responses to natural images. Vis Res. 2008;48(12):1427–439.PubMedCrossRefGoogle Scholar
  25. 25.
    Horaud R, Monga O. Vision par ordinateur: outils fondamentaux. Paris: Editions Hermès; 1995. p. 426. ISBN:978-2866014810.Google Scholar
  26. 26.
    Howard IP, Rogers BJ. Depth perception. Oxford: Oxford Scholarship; 2008.Google Scholar
  27. 27.
    Hubel D, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol. 1962;160:106–54.Google Scholar
  28. 28.
    Hubel D, Wiesel TN. Stereoscopic vision in macaque monkey. Cells sensitive to binocular depth in area 18 of the macaque monkey cortex. J Nat. 1970;225:41–2.Google Scholar
  29. 29.
    Jones JP, Palmer LA. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol. 1987;58(6):1233–258.PubMedGoogle Scholar
  30. 30.
    Kingsbury N. Image processing with complex wavelets. Phil Trans Royal Soc Lond A. 1997;357:2543–560.CrossRefGoogle Scholar
  31. 31.
    Kuffler SW. Discharge patterns and functional organization of mammalian retina. J Physiol. 1953;16:37–68.Google Scholar
  32. 32.
    Lehky SR, Maunsell JH. No binocular rivalry in the lgn of alert macaque monkeys. Vis Res. 1996;36(9):1225–234.PubMedCrossRefGoogle Scholar
  33. 33.
    Liu A, Gaska JP, Jacobson LD, Pollen DA. Interneuronal interaction between members of quadrature phase and anti-phase pairs in the cat’s visual cortex. Vis Res. 1992;32(1193–198).Google Scholar
  34. 34.
    Mallat S. A theory for multiresolution signal decomposition : the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–93.CrossRefGoogle Scholar
  35. 35.
    Mallat S, Peyrè G. Bandelet image approximation and compression. SIAM Multiscale Model Simul. 2005;4(3):992–1039.CrossRefGoogle Scholar
  36. 36.
    Mallat S, Peyrè G. Orthogonal bandelet bases for geometric image approximation. Commun Pure Appl Math. 2006;61(9):1173–212.Google Scholar
  37. 37.
    Marr D, Poggio T. A computational theory of human stereo vision. Proc Royal Soc Lond Ser B Biol Sci. 1979;204(1156):301–28.CrossRefGoogle Scholar
  38. 38.
    McLoughlin NP, Grossberg S. Cortical computation of stereo disparity. Vis Res. 1998;38(1):91–9.PubMedCrossRefGoogle Scholar
  39. 39.
    Nakayama K, Shimojo S. Da vinci stereopsis: depth and subjective occluding contours from unpaired image points. Neural Netw. 1990;30:1811–25.Google Scholar
  40. 40.
    Nasrabadi MN, Clifford SP, Liu Y. Integration of stereo vision and optical flow by using an energy-minimization approach. J Opt Soc Am. 1989;6(6):900–7.CrossRefGoogle Scholar
  41. 41.
    Ohzawa I, DeAngelis G, Freeman R. Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science. 1990;249:1037–41.PubMedCrossRefGoogle Scholar
  42. 42.
    Ohzawa I, Freeman R. The binocular organization of complex cells in the cat’s visual cortex. J Neurophysiol. 1986;56: 243–59.PubMedGoogle Scholar
  43. 43.
    Ohzawa I, Freeman R. The binocular organization of simple cells in the cat’s visual cortex. J Neurophysiol. 1986;56: 221–42.PubMedGoogle Scholar
  44. 44.
    Palmer L, Davis T. Receptive-field structure in cat striate cortex. Vis Res. 1981;46:260–76.Google Scholar
  45. 45.
    Pollen D, Ronner S. Phase relationships between adjacent simple cells in the visual cortex. Science. 1981;212:1409–11.Google Scholar
  46. 46.
    Rock I. La perception. Brussels: De Boeck; 2001.Google Scholar
  47. 47.
    Schanda J. Colorimetry: understanding the CIE system. Hoboken, NJ: Wiley; 2007.CrossRefGoogle Scholar
  48. 48.
    Selesnick IW, Baraniuk RG, Kingsbury NG. The dual-tree complex wavelet transform. IEEE signal processing magazine; 2005. pp. 123–51.Google Scholar
  49. 49.
    Shimojo S, Nakayama K. Real world occlusion constraints and binocular disparity. Vis Res. 1990;30(1):69–80.PubMedCrossRefGoogle Scholar
  50. 50.
    Watanabe O, Fukushima K. Stereo algorithm that extracts a depth cue from interocularly unpaired points. Neural Netw (1999);12:569–78.PubMedCrossRefGoogle Scholar
  51. 51.
    Yasui S. On the square root intensity coding at the level of cone photoreceptors. Vis Res. 1992);32(1):199–202.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.XLIM-SICUniversity of PoitiersPoitiersFrance

Personalised recommendations