Article

Detection of object-motion during self-motion is predicted by local motion direction differences not deviation from the focus of expansion

Authors:

Paul R. MacNeilage,

Martin S. BanksAuthors Info & Claims

APGV '05: Proceedings of the 2nd symposium on Applied perception in graphics and visualization

Page 163

https://doi.org/10.1145/1080402.1080443

Published: 26 August 2005 Publication History

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Abstract

Motion on the retina can arise from 1) object motion relative to the observer and 2) self-motion relative to stationary objects. These very different events can result in similar retinal-image motion, so telling the difference is a difficult, but important problem.

References

[1]

Gray, R., Macuga, K., and Regan, D. Long range interactions between object-motion and self-motion in the perception of movement in depth, Vision Research44, 179--195 (2004).

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[2]

Royden, C. S. and Hildreth, E. C. Human heading judgments in the presence of moving objects, Perception and Psychophysics58 (6), 836--856 (1996).

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[3]

Warren, W. H. and Saunders J. A. Perceiving heading in the presence of moving objects, Perception24(3), 315--331 (1995).

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Detection of object-motion during self-motion is predicted by local motion direction differences not deviation from the focus of expansion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
    1. Animation

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Information

Published In

APGV '05: Proceedings of the 2nd symposium on Applied perception in graphics and visualization

August 2005

187 pages

ISBN:1595931392

DOI:10.1145/1080402

Conference Chairs:
Heinrich Bülthoff
Max-Planck Institute for Biological Cybernetics
,
Tom Troscianko
University of Bristol

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2005

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APGV05

Sponsor:

SIGGRAPH

APGV05: ACM Symposium on Applied Perception in Graphics and Visualization 2005

August 26 - 28, 2005

A Coroña, Spain

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Overall Acceptance Rate 19 of 33 submissions, 58%

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