Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Computing the direction of heading from affine image flow

J M Beusmans1

  • 1Center for Neural Science, New York University, NY 10003.

Biological Cybernetics
|January 1, 1993
PubMed
Summary

This study introduces an optic flow heading algorithm using affine flow, offering robustness against observer rotation and image transformations. It enhances heading accuracy with greater environmental depth variation.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Decreased internalisation of erbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib.

Systems biology·2006
Same author

Computational modelling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity.

Systems biology·2006
Same author

From vision to action: experiments and models of steering control during driving.

Journal of experimental psychology. Human perception and performance·2000
Same author

Perceived object shape affects the perceived direction of self-movement.

Perception·1999
Same author

Optic flow and the metric of the visual ground plane.

Vision research·1998
Same author

Chromatic properties of neurons in macaque MT.

Visual neuroscience·1994

Area of Science:

  • Visual perception
  • Computational neuroscience
  • Robotics

Background:

  • Optic flow is crucial for determining heading direction during self-motion.
  • Current heading algorithms rely on Cartesian flow fields, measuring feature displacement over time.

Purpose of the Study:

  • To explore a novel heading algorithm utilizing affine flow.
  • To compare the performance of affine flow algorithms against Cartesian flow and least-squares methods.
  • To identify characteristics of the affine flow algorithm that may indicate its use by the human visual system.

Main Methods:

  • Developed a heading algorithm based on affine flow, defined as feature displacement modulo an affine transformation.
  • Modeled observer motion using translation and rotation, analyzing tangent translational field lines on the viewing sphere.
  • Compared affine flow algorithm performance with differential Cartesian flow and least-squares search algorithms.

Main Results:

  • Affine flow forms a radial field centered on the heading direction, independent of observer rotation.
  • The algorithm demonstrates immunity to arbitrary affine transformations of input images.
  • Accuracy improves with increased environmental depth variation; heading cannot be recovered in single-plane environments.

Conclusions:

  • The affine flow heading algorithm offers unique properties, such as rotation immunity and depth-dependent accuracy.
  • These characteristics provide testable predictions for determining if the human visual system employs affine flow for heading perception.
  • Further research can compare affine flow with differential Cartesian motion approximations.

Related Experiment Videos