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Globally convergent autocalibration using interval analysis.

Andrea Fusiello1, Arrigo Benedetti, Michela Farenzena

  • 1Dipartimento di Informatica, Universita degli studi di Verona, 1-37134 Verona, Italy. fusiello@sci.univr.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 3, 2004
PubMed
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This study introduces a novel autocalibration method for moving cameras using interval branch-and-bound. It guarantees convergence to the global solution for unknown intrinsic parameters, unlike traditional methods.

Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Computing

Background:

  • Autocalibration of moving cameras is crucial for 3D reconstruction and scene understanding.
  • Existing methods often rely on numerical optimization with uncertain convergence.
  • Unknown intrinsic camera parameters pose a significant challenge.

Purpose of the Study:

  • To develop a robust autocalibration method for moving cameras with unknown constant intrinsic parameters.
  • To guarantee convergence to the global solution for camera autocalibration.
  • To improve the reliability of camera parameter estimation.

Main Methods:

  • Utilized an interval branch-and-bound method for guaranteed global minimization.
  • Employed Interval Analysis for mathematical certainty and arbitrary accuracy.

Related Experiment Videos

  • Based the cost function on the Huang-Faugeras constraint of the essential matrix.
  • Investigated Bernstein polynomial forms for accelerated solution search.

    • Main Results:

    • The proposed interval branch-and-bound method ensures convergence to the global solution.
    • The method requires only point correspondences and a search interval as input.
    • Experimental results demonstrate the effectiveness of the developed technique.
    • Bernstein polynomial forms show potential for speeding up the solution search.

    Conclusions:

    • The interval branch-and-bound approach offers a mathematically certain and accurate solution for camera autocalibration.
    • This method overcomes the convergence limitations of existing numerical optimization techniques.
    • The findings contribute to more reliable camera parameter estimation in computer vision applications.