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Automated Measurement of Geometric Features in Curvilinear Structures Exploiting Steger's Algorithm.

Nicola Giulietti1, Paolo Chiariotti1, Gian Marco Revel2

  • 1Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy.

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Summary
This summary is machine-generated.

This study automates parameter selection for Steger's ridge detection algorithm, enabling precise geometric measurements of curvilinear structures in images for applications like defect analysis and quality control.

Keywords:
Steger algorithmgeometric feature measurementline width measurementvision-based measurement

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Area of Science:

  • Computer Vision
  • Image Analysis
  • Metrology

Background:

  • Accurate geometric assessment of curvilinear structures is crucial for various vision-based measurement systems.
  • Current applications of Steger's ridge detection algorithm are limited by manual input parameter selection.
  • Automating this selection is key for broader adoption in measurement fields.

Purpose of the Study:

  • To develop a fully automated approach for selecting input parameters of Steger's ridge detection algorithm.
  • To enable automated vision-based measurement systems for curvilinear structures.
  • To assess the metrological performance of the proposed automated method.

Main Methods:

  • An automated parameter selection approach for Steger's ridge detection algorithm is proposed.
  • The method's effectiveness is validated using both synthesized and experimental image data.
  • Metrological performance is rigorously discussed.

Main Results:

  • The proposed method successfully automates the parameter selection phase for Steger's algorithm.
  • Demonstrated applicability on diverse datasets, including real-world defect analysis scenarios.
  • The automated approach maintains or improves metrological accuracy.

Conclusions:

  • The developed automated parameter selection enhances the usability of Steger's algorithm for curvilinear structure measurement.
  • This advancement facilitates the development of fully automated vision-based measurement systems.
  • The approach shows significant potential for quality control, defect analysis, and other imaging applications.