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A computational theory of human stereo vision

  • D. Marr
  • T. Poggio
  • Ellen C. Hildreth
  • W. Eric L. Grimson

Abstract

An algorithm is proposed for solving the stereoscopic matching problem. The algorithm consists of five steps: (1) Each image is filtered at different orientations with bar masks of four sizes that increase with eccentricity; the equivalent filters are one or two octaves wide. (2) Zero-crossings in the filtered images, which roughly correspond to edges, are localized. Positions of the ends of lines and edges are also found. (3) For each mask orientation and size, matching takes place between pairs of zero-crossings or terminations of the same sign in the two images, for a range of disparities up to about the width of the mask’s central region. (4) Wide masks can control vergence movements, thus causing small masks to come into correspondence. (5) When a correspondence is achieved, it is stored in a dynamic buffer, called the 2½-D sketch.

Keywords

Receptive Field Subjective Contour Receptive Field Size False Target Line Spread Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Birkhäuser Boston 1991

Authors and Affiliations

  • D. Marr
    • 1
  • T. Poggio
    • 2
  • Ellen C. Hildreth
    • 3
    • 4
  • W. Eric L. Grimson
    • 5
  1. 1.Psychology DepartmentM.I.T.CambridgeUSA
  2. 2.Max-Planck-Institut für Biologische KybernetikTübingenGermany
  3. 3.Brain and Cognitive SciencesArtificial Intelligence LaboratoryUSA
  4. 4.Center for Biological Information ProcessingMassachusetts Institute of TechnologyCambridgeUSA
  5. 5.Electrical Engineering and Computer Sciences, Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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