Two-Dimensional and Three-Dimensional Computer Vision

  • Michael A. Michael A.
  • Jean-José Orteu
  • Hubert W. Schreier

Two-dimensional (2D) and three-dimensional (3D) computer vision employ the pinhole camera model, distortion models and general optimization procedures described in Chapter 3. For two-dimensional computer vision, it is assumed that the motions of a planar object occur within the object plane. In most 2D cases, the object plane is nominally parallel to the camera sensor plane. In 3D computer vision,1 the only restrictions placed on the motion of a curvilinear object are (a) the object remains in focus during the motion and (b) points of interest on the object are imaged onto two or more camera sensor planes. In the following sections, details regarding models and calibration procedures for 2D and 3D computer vision are provided.


Image Point Planar Object Intrinsic Parameter Bundle Adjustment Extrinsic Parameter 
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

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.University of South CarolinaDepartment of Mechanical EngineeringColumbiaUSA
  2. 2.Ecoles des Mines d’AlbiCampus JarlardAlbiFrance
  3. 3.Correlated Solutions, Inc.West ColumbiaUSA

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