Perception of 3D Motion through Patterns of Visual Motion.

Abstract

Geometric considerations suggest that the problem of estimating a system's three-dimensional (3D) motion from a sequence of images, which has puzzled researchers in the fields of Computational Vision and Robotics as well as the Biological Sciences, can be addressed as a pattern recognition problem. Information for constructing the relevant patterns is found in spatial arrangements or gratings, that is, aggregations of orientations along which retinal motion information is estimated. The exact form of the gratings is defined by the shape of the retina or imaging surface; for a planar retina they are radial lines, concentric circles, as well as elliptic and hyperbolic curves, while for a spherical retina they become longitudinal and latitudinal circles for various axes. Considering retinal motion information computed normal to these gratings, patterns are found that have encoded in their shape and location on the retina subsets of the 3D motion parameters. The importance of these patterns is first that they depend only on the 3D motion and not on the scene in view, and second that they utilize only the sign of image motion along a set of directions defined by the gratings.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1995
Accession Number
ADA295097

Entities

People

  • Cornelia Fermüller
  • Yiannis Aloimonos

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Biological Sciences
  • Cognitive Science
  • Computer Science
  • Computer Vision
  • Computers
  • Contracts
  • Coordinate Systems
  • Flow Fields
  • Geometric Forms
  • Geometry
  • Measurement
  • Orientation (Direction)
  • Pattern Recognition
  • Perception
  • Three Dimensional
  • Two Dimensional

Readers

  • Fluid Dynamics.
  • Image Processing and Computer Vision.
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy