Stereo Feature Matching in Disparity Space

Abstract

This paper describes a new method for matching, validating, and disambiguating features for stereo vision. It is based on the Marr-Poggio- Grimson stereo matching algorithm which uses zero-crossing contours in difference of Gaussian filtered images as features. The matched contours are represented in disparity space, which makes the information needed for matched contour validation and disambiguation easily accessible. The use of disparity space also makes the algorithm conceptually cleaner than previous implementations of the Marr-Poggio-Grimson algorithm and yields a more efficient matching process. Keywords: Stereo vision; Artificial intelligence; Algorithms; Stereo matching; Disparity space.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1989
Accession Number
ADA218170

Entities

People

  • David J. Braunegg

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Stereo Vision
  • Consistency
  • Continuity
  • Convolution
  • Crossings
  • Department Of Defense
  • Disparities
  • Geometry
  • Information Systems
  • Lists (Data Structures)
  • Military Research
  • Three Dimensional
  • Two Dimensional
  • Validation

Readers

  • Computer Vision.

Technology Areas

  • AI & ML
  • Space