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Carlos Ricolfe-Viala1, Antonio-Jose Sanchez-Salmeron, Enrique Martinez-Berti

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Summary
This summary is machine-generated.

This study introduces an inverted camera calibration method. By performing nonlinear minimization first, it improves accuracy and avoids common issues in traditional camera calibration techniques.

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Area of Science:

  • Computer Vision
  • Geometric Modeling

Background:

  • Traditional camera calibration involves linear approximation followed by nonlinear minimization.
  • Coupled pin-hole and lens distortion models in nonlinear minimization can lead to local solutions and ill-conditioning.
  • Simultaneous calculation of parameters with different units (pixels, world coordinates, distortion) degrades conditioning.

Purpose of the Study:

  • To propose an inverted camera calibration method that overcomes limitations of traditional approaches.
  • To improve the accuracy and robustness of camera parameter estimation.
  • To decouple pin-hole and lens distortion models during calibration.

Main Methods:

  • Inverted calibration sequence: nonlinear minimization first, followed by linear computation of camera parameters.
  • Utilizing projective geometry constraints within nonlinear minimization to correct detected image point locations.
  • Separate computation of pin-hole and lens distortion models using corrected point locations.

Main Results:

  • The proposed method avoids the coupling between pin-hole and lens distortion models.
  • Improved conditioning of the nonlinear minimization process is achieved.
  • Accurate camera parameter estimation by addressing local minima and ill-conditioning issues.

Conclusions:

  • The inverted camera calibration method offers a more robust and accurate alternative to traditional techniques.
  • Decoupling models and correcting point locations enhances the overall calibration performance.
  • This approach is beneficial for applications requiring precise camera intrinsic and extrinsic parameter estimation.