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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.

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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.

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