Skip to main content
SpringerLink
Log in

Optic Flow

  • Living reference work entry
  • First Online:
Encyclopedia of Animal Cognition and Behavior

  • 89 Accesses

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...

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • 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.

    Article  Google Scholar 

  • Borst, A., & Helmstaedter, M. (2015). Common circuit design in fly and mammalian motion vision. Nature Neuroscience, 18, 1067–1076.

    Article  PubMed  Google Scholar 

  • Franceschini, N. (2014). Small brains, smart machines: From fly vision to robot vision and back again. Proceedings of the IEEE, 102, 751–781.

    Article  Google Scholar 

  • 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 

  • Krapp, H. G., & Hengstenberg, R. (1996). Estimation of self-motion by optic flow processing in single visual interneurons. Nature, 384, 463–466.

    Article  PubMed  Google Scholar 

  • Land, M. F., & Collett, T. S. (1974). Chasing behaviour of houseflies (Fannia cannicularis). A description and analysis. Journal of Comparative Physiology, 89, 331–357.

    Article  Google Scholar 

  • Lee, D. N., & Reddish, P. E. (1980). Plummeting gannets: A paradigm of ecological optics. Nature, 293, 293–294.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Srinivasan, M. V. (2011b). Visual control of navigation in insects and its relevance for robotics. Current Opinion in Neurobiology, 21, 535–543.

    Article  PubMed  Google Scholar 

  • Srinivasan, M. V., Poteser, M., & Kral, K. (1999). Motion detection in insect vision and navigation. Vision Research, 39, 2749–2766.

    Article  PubMed  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Sun, H., & Frost, B. J. (1998). Computation of different optical variables of looming objects in pigeon nucleus rotundus neurons. Nature Neuroscience, 1, 296–303.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Queensland Brain Institute and School of Information Technology and Electrical Engineering. The University of Queensland, St Lucia, QLD, 4072, Australia

    Mandyam V. Srinivasan

Authors
  1. Mandyam V. Srinivasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mandyam V. Srinivasan .

Editor information

Editors and Affiliations

  1. Oakland University , Rochester, Michigan, USA

    Jennifer Vonk

  2. Department of Psychology, Oakland University Department of Psychology, Rochester, Michigan, USA

    Todd Shackelford

Section Editor information

  1. Hofstra University, Long Island, USA

    Oskar Pineno

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

Srinivasan, M.V. (2017). Optic Flow. In: Vonk, J., Shackelford, T. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-47829-6_1299-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47829-6_1299-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47829-6

  • Online ISBN: 978-3-319-47829-6

  • eBook Packages: Springer Reference Behavioral Science and PsychologyReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation