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Related Experiment Video

Updated: Jun 16, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

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Published on: October 27, 2016

Data filtering with support vector machines in geometric camera calibration.

B Ergun1, T Kavzoglu, I Colkesen

  • 1Gebze Institute of Technology, Department of Geodetic and Photogrammetric Engineering, Muallimkoy Campus, 41400 Gebze-Kocaeli, Turkey. bergun@gyte.edu.tr

Optics Express
|February 23, 2010
PubMed
Summary

Support Vector Machines (SVMs) offer a robust method for calibrating non-metric cameras, improving 3D metric information extraction in photogrammetry. This approach models lens distortions effectively for accurate on-the-job camera calibration.

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Last Updated: Jun 16, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Photogrammetry
  • Computer Vision
  • Geomatics Engineering

Background:

  • Non-metric digital cameras are increasingly used in close-range photogrammetry and machine vision.
  • Accurate camera calibration is essential for reliable 3D metric information extraction.
  • Existing methods often rely on perspective geometrical models and bundle adjustment.

Purpose of the Study:

  • To introduce an alternative approach for on-the-job photogrammetric calibration of non-metric cameras.
  • To model lens distortions using Support Vector Machines (SVMs).
  • To evaluate the effectiveness of SVMs in improving geometric calibration accuracy.

Main Methods:

  • Employed Support Vector Machines (SVMs) with a radial basis function kernel to model lens distortions.
  • Applied the SVMs model to the geometric calibration process for an Olympus E10 camera.
  • Utilized bundle adjustment with additional parameters for experimental estimation using a DSLR camera at three focal lengths (9, 18, 36 mm).

Main Results:

  • The SVMs approach demonstrated robustness in correcting image coordinates by modeling total distortions.
  • Analyses based on object point discrepancies and standard errors confirmed the effectiveness of the SVMs method.
  • Successful on-the-job calibration was achieved using a limited number of images.

Conclusions:

  • SVMs provide a viable and effective alternative for the photogrammetric calibration of non-metric cameras.
  • This method enhances the accuracy of 3D metric information extraction, particularly in on-the-job scenarios.
  • The study highlights the potential of machine learning techniques in improving camera calibration processes.