Encyclopedia of Animal Cognition and Behavior

Living Edition
| Editors: Jennifer Vonk, Todd Shackelford

Optic Flow

  • Mandyam V. Srinivasan
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-47829-6_1299-1

Introduction

When an animal moves, the image of its surrounding environment moves in the retinae of the animal’s eyes. The pattern of image motion (known as the “optic flow field,” OFF) bears rich information about the animal’s own motion (termed “egomotion”), about the distance to various nearby objects, the speed of locomotion, the distance the animal has traveled, and several other parameters. This entry highlights some of the cues that are contained in the optic flow field and describes how they are used to control locomotion and enable safe and accurate navigation through the environment.

The Relation of Optic Flow to Egomotion

When a flying animal turns (yaws) to the left, the image of the world appears to move to the right and vice versa. The OFF generated by a leftward yaw (counterclockwise rotation about the dorso-ventral axis) is shown in Fig. 1a, where the individual vectors depict the direction and magnitude of the image motion at each location in the animal’s visual field...
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References

  1. Baird, E., Boeddeker, N., Ibbotson, M. R., & Srinivasan, M. V. (2013). A universal strategy for visually guided landing. Proceedings of the National Academy of Sciences, 110, 18686–18691.CrossRefGoogle Scholar
  2. Borst, A., & Helmstaedter, M. (2015). Common circuit design in fly and mammalian motion vision. Nature Neuroscience, 18, 1067–1076.CrossRefPubMedGoogle Scholar
  3. Franceschini, N. (2014). Small brains, smart machines: From fly vision to robot vision and back again. Proceedings of the IEEE, 102, 751–781.CrossRefGoogle Scholar
  4. Gunasinghe, D., Strydom, R., & Srinivasan, M. V. (2016). A mid-air collision warning system: Vision-based estimation of collision threats for aircraft. In Proceedings of the Australasian conference on robotics and automation, The University of Queensland, Brisbane, Paper 111 S1, 5–7 Dec 2016.Google Scholar
  5. Krapp, H. G., & Hengstenberg, R. (1996). Estimation of self-motion by optic flow processing in single visual interneurons. Nature, 384, 463–466.CrossRefPubMedGoogle Scholar
  6. Land, M. F., & Collett, T. S. (1974). Chasing behaviour of houseflies (Fannia cannicularis). A description and analysis. Journal of Comparative Physiology, 89, 331–357.CrossRefGoogle Scholar
  7. Lee, D. N., & Reddish, P. E. (1980). Plummeting gannets: A paradigm of ecological optics. Nature, 293, 293–294.CrossRefGoogle Scholar
  8. Liu, R.-F., Niu, Y.-Q., & Wang, S.-R. (2008). Thalamic neurons in the pigeon compute distance-to-collision of an approaching surface. Brain, Behaviour and Evolution, 72, 37–47.CrossRefGoogle Scholar
  9. Srinivasan, M. V. (2011a). Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics. Physiological Reviews, 91, 389–411.CrossRefGoogle Scholar
  10. Srinivasan, M. V. (2011b). Visual control of navigation in insects and its relevance for robotics. Current Opinion in Neurobiology, 21, 535–543.CrossRefPubMedGoogle Scholar
  11. Srinivasan, M. V., Poteser, M., & Kral, K. (1999). Motion detection in insect vision and navigation. Vision Research, 39, 2749–2766.CrossRefPubMedGoogle Scholar
  12. Srinivasan, M. V., Moore, R. J. D., Thurrowgood, S., Soccol, D., Bland, D., & Knight, M. (2013). Vision and navigation in insects, and applications to aircraft guidance. In J. S. Werner & L. M. Chalupa (Eds.), The visual neurosciences (pp. 1219–1232). Cambridge, MA: MIT Press.Google Scholar
  13. Strydom, R., Denuelle, A., & Srinivasan, M. (2016). Bio-inspired principles applied to the guidance, navigation and control of UAS. Aerospace, 3(3), 21. https://doi.org/10.3390/aerospace3030021.CrossRefGoogle Scholar
  14. Sun, H., & Frost, B. J. (1998). Computation of different optical variables of looming objects in pigeon nucleus rotundus neurons. Nature Neuroscience, 1, 296–303.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Queensland Brain Institute and School of Information Technology and Electrical Engineering. The University of QueenslandSt LuciaAustralia

Section editors and affiliations

  • Oskar Pineno
    • 1
  1. 1.Hofstra UniversityLong IslandUSA