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Related Experiment Video

Updated: Aug 16, 2025

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
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Interpretation and Transformation of Intrinsic Camera Parameters Used in Photogrammetry and Computer Vision.

Kuan-Ying Lin1, Yi-Hsing Tseng1, Kai-Wei Chiang1

  • 1Department of Geomatics, National Cheng Kung University, No. 1, Daxue Road, East District, Tainan City 701, Taiwan.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study clarifies intrinsic camera parameter (ICP) definitions in photogrammetry (PH) and computer vision (CV), proposing a novel transformation algorithm. The method rigorously converts ICPs between PH and CV models, improving accuracy and enabling mixed-software use.

Keywords:
camera calibrationcamera mathematical modelcomputer visionintrinsic camera parametersphotogrammetry

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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Geometric Modeling

Background:

  • Intrinsic camera geometry modeling differs between photogrammetry (PH) and computer vision (CV).
  • Discrepancies in intrinsic camera parameter (ICP) definitions (focal length, principal point, distortion, etc.) hinder cross-disciplinary research and software integration.
  • Existing ICP conversion methods lack rigor and can lead to confusion.

Purpose of the Study:

  • To clarify and reconcile differing ICP definitions across photogrammetry and computer vision.
  • To propose a novel, rigorous algorithm for transforming ICPs between PH and CV models.
  • To enable seamless integration and application of mixed software from both fields.

Main Methods:

  • Developed an ICP transformation algorithm utilizing least-squares adjustment.
  • Applied image points with ICPs defined in both PH and CV frames for conversion.
  • Validated the algorithm by calibrating two cameras and comparing results with conventional methods.

Main Results:

  • The proposed ICP transformation algorithm successfully converts parameters between PH and CV models.
  • Experimental results demonstrate improved accuracy and performance compared to state-of-the-art methods.
  • Rectified images and distortion plots confirm the effectiveness of the ICP conversions.

Conclusions:

  • The novel ICP transformation method provides a rigorous solution for parameter conversion between photogrammetry and computer vision.
  • This facilitates more accurate vision-based measurements and enables flexible use of diverse calibration software.
  • The study confirms significant performance improvements in ICP conversions, benefiting cross-disciplinary applications.