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A Closer Look at the Visual Input to Self-Motion Estimation

  • Chapter
Motion Vision

Abstract

In their Chapter, Dahmen et al. use an interesting approach to the self-motion (egomotion) estimation problem that combines traditional vector flow field decomposition schemes with a matched filter (template) scheme. In the field of visual self-motion perception these two approaches represent two different schools of thought about how biological systems solve the self-motion problem. The boundaries between these classes of models occasionally become blurred but there is one feature that distinguishes these two approaches and which can be used to assess their pros and cons: namely the type of input they are designed to process. In this section I examine the different input requirements of the two model types and identify some of the challenges that are faced by modellers in this area.

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Author information

Authors and Affiliations

  1. Psychology Department, The University of Waikato, Hamilton, New Zealand

    John A. Perrone

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  1. John A. Perrone
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Editors and Affiliations

  1. Centre for Visual Sciences Research School of Biological Sciences, Australian National University, PO Box 475, Canberra, ACT 2601, Australia

    Johannes M. Zanker  & Jochen Zeil  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Perrone, J.A. (2001). A Closer Look at the Visual Input to Self-Motion Estimation. In: Zanker, J.M., Zeil, J. (eds) Motion Vision. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56550-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-56550-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62979-2

  • Online ISBN: 978-3-642-56550-2

  • eBook Packages: Springer Book Archive

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