Visual Perception of Mixed Homogeneous Textures in Flying Pigeons

  • Margarita Zaleshina
  • Alexander ZaleshinEmail author
  • Adriana Galvani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


In this study, we simulated the visual perception of the terrain in flying pigeons over combined homogeneous terrain with multiple textures – forest and grassland, water surface and seacoast. The surfaces along the pigeon’s flight trajectory were considered as mixed textures observed from a bird’s eye view. In the proposed method, the main structural elements for the analyzed textures were selected and then statistically homogeneous characteristics of the texture were determined. The textural characteristics and their changes during flight were recorded in the form of distinct “event channels”. For different types of terrain, the frequency characteristics of visual perception were calculated and compared. In addition, we considered the possibility of comparing the frequency characteristics of the textures with data regarding the pigeon’s rhythmic brain activity. Spatial data—open-access remote sensing datasets—were processed using the geographical information system QGIS. Our results show that recognizing mixed landscape textures can help solve navigation tasks when flying over terrain with sparse landmarks.


Visual perception Spatial navigation Brain activity 


  1. 1.
    Bovet, D., Vauclair, J.: Picture recognition in animals and humans. Behav. Brain Res. 109, 143–165 (2000)CrossRefGoogle Scholar
  2. 2.
    D’Eath, R.B.: Can video images imitate real stimuli in animal behaviour experiments? Biol. Rev. 73, 267–292 (1998). Scholar
  3. 3.
    Avargues-Weber, A., Dyer, A.G., Ferrah, N., Giurfa, M.: The forest or the trees, preference for global over local image processing is reversed by prior experience in honeybees. Proc. Biol. Sci. 282, 20142384 (2015)CrossRefGoogle Scholar
  4. 4.
    Leonhardt, S.D., Kaluza, B.F., Wallace, H., Heard, T.A.: Resources or landmarks, which factors drive homing success in Tetragonula carbonaria foraging in natural and disturbed landscapes? J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 202, 701–708 (2016)CrossRefGoogle Scholar
  5. 5.
    Krekelberg, B., van Wezel, R.J.A.: Neural mechanisms of speed perception, transparent motion. J. Neurophysiol. 110, 2007–2018 (2013)CrossRefGoogle Scholar
  6. 6.
    Gagliardo, A., Ioale, P., Savini, M., Dell’Omo, G., Bingman, V.P.: Hippocampal-dependent familiar area map supports corrective re-orientation following navigational error during pigeon homing: a GPS-tracking study. Eur. J. Neurosci. 29, 2389–2400 (2009)CrossRefGoogle Scholar
  7. 7.
    Freeman, J., Ziemba, C.M., Heeger, D.J., Simoncelli, E.P., Movshon, J.A.: A functional and perceptual signature of the second visual area in primates. Nat. Neurosci. 16, 974–981 (2013)CrossRefGoogle Scholar
  8. 8.
    Ziemba, C.M., Freeman, J., Movshon, J.A., Simoncelli, E.P.: Selectivity and tolerance for visual texture in macaque V2. Proc. Natl. Acad. Sci. 201510847 (2016)Google Scholar
  9. 9.
    Lu, H.D., Chen, G., Tanigawa, H., Roe, A.W.: A motion direction map in macaque V2. Neuron 68, 1002–1013 (2010). Scholar
  10. 10.
    Horiuchi, T.K., Koch, C.: Analog VLSI-based modeling of the primate oculomotor system. Neural Comput. 11, 243–265 (1999)CrossRefGoogle Scholar
  11. 11.
    Jimenez Ortega, L., Stoppa, K., Gunturkun, O., Troje, N.F.: Vision during head bobbing, are pigeons capable of shape discrimination during the thrust phase? Exp. Brain Res. 199, 313 (2009). Scholar
  12. 12.
    Biro, D., Freeman, R., Meade, J., Roberts, S., Guilford, T.: Pigeons combine compass and landmark guidance in familiar route navigation. Proc. Natl. Acad. Sci. U. S. A. 104, 7471–7476 (2007)CrossRefGoogle Scholar
  13. 13.
    Schiffner, I., Siegmund, B., Wiltschko, R.: Following the Sun, a mathematical analysis of the tracks of clock-shifted homing pigeons. J. Exp. Biol. 217, 2643–2649 (2014)CrossRefGoogle Scholar
  14. 14.
    Vyssotski, A.L., Dell’Omo, G., Dell’Ariccia, G., Abramchuk, A.N., Serkov, A.N., Latanov, A.V., et al.: EEG responses to visual landmarks in flying pigeons. Curr. Biol. 19, 1159–1166 (2009)CrossRefGoogle Scholar
  15. 15.
    Mann, R.P., Armstrong, C., Meade, J., Freeman, R., Biro, D., Guilford, T.: Landscape complexity influences route-memory formation in navigating pigeons. Biol. Lett. 10, 20130885 (2014)CrossRefGoogle Scholar
  16. 16.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification (1973)Google Scholar
  17. 17.
    Webster, M.A., De Valois, K.K., Switkes, E.: Orientation and spatial-frequency discrimination for luminance and chromatic gratings. J. Opt. Soc. Am. A 7, 1034–1049 (1990)CrossRefGoogle Scholar
  18. 18.
    Zaccolo, M.: Good features to track. Methods Mol. Biol. 178, 255–258 (2002)Google Scholar
  19. 19.
    Barbot, A., Landy, M.S., Carrasco, M.: Differential effects of exogenous and endogenous attention on second-order texture contrast sensitivity. J. Vis. 12, 1–15 (2012)CrossRefGoogle Scholar
  20. 20.
    Song, B., Li, P., Li, J., Plaza, A.: One-class classification of remote sensing images using kernel sparse representation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9, 1613–1623 (2016)CrossRefGoogle Scholar
  21. 21.
    Moody, D.I., Brumby, S.P., Rowland, J.C., Altmann, G.L.: Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries. In: Huang, B., Chang, C.-I., Lopez, J.F. (eds.) Proceedings of the SPIE, vol. 9124, p. 91240Y (2014)Google Scholar
  22. 22.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010). Scholar
  23. 23.
    Du, P., Xia, J., Zhang, W., Tan, K., Liu, Y., Liu, S.: Multiple classifier system for remote sensing image classification. Sensors 12, 4764–4792 (2012)CrossRefGoogle Scholar
  24. 24.
    Skowronek, S., Asner, G.P., Feilhauer, H.: Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy. Ecol. Inform. 37, 66–76 (2017)CrossRefGoogle Scholar
  25. 25.
    Joseph, J.S., Victor, J.D., Optican, L.M.: Scaling effects in the perception of higher-order spatial correlations. Vis. Res. 37, 3097–3107 (1997)CrossRefGoogle Scholar
  26. 26.
    Kingdom, F.A.A., Keeble, D.R.T.: Luminance spatial frequency differences facilitate the segmentation of superimposed textures. Vis. Res. 40, 1077–1087 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Margarita Zaleshina
    • 1
  • Alexander Zaleshin
    • 1
    Email author
  • Adriana Galvani
    • 2
  1. 1.Moscow Institute of Physics and TechnologyMoscowRussia
  2. 2.University of BolognaBolognaItaly

Personalised recommendations