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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Neural network digital fringe calibration technique for structured light profilometers.

Matthew J Baker1, Jiangtao Xi, Joe F Chicharo

  • 1School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, New South Wales, Australia. mjb06@uow.edu.au

Applied Optics
|February 24, 2007
PubMed
Summary

This study introduces a new neural network calibration method to enhance structured light profilometers. The technique significantly improves accuracy by correcting distorted fringe data, enabling faster and more precise 3D measurements.

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Machine Learning

Background:

  • Structured light profilometry is crucial for 3D measurements but often suffers from reduced accuracy due to aberrated pattern intensity distributions.
  • Existing calibration methods may be complex or require extensive data, limiting their application in rapid profiling scenarios.

Purpose of the Study:

  • To develop a novel neural network-based signal calibration technique for digital projection-based structured light profilometers.
  • To address the challenge of aberrated pattern intensity distributions that degrade profilometer performance.
  • To improve the accuracy and efficiency of 3D measurements using structured light techniques.

Main Methods:

  • A feed-forward backpropagation neural network was employed for a signal mapping approach.
  • The neural network was trained to map distorted fringe data to nondistorted data, leveraging its generalization and interpolation capabilities.
  • The technique was validated through both simulation and experimental testing.

Main Results:

  • Simulation results demonstrated a potential accuracy improvement exceeding 80%.
  • The proposed calibration method requires only a single image cross-section for calibration.
  • Experimental validation confirmed the effectiveness of the neural network approach in correcting signal aberrations.

Conclusions:

  • The novel neural network signal calibration technique effectively enhances the performance of structured light profilometers.
  • This method offers a significant improvement in measurement accuracy and is suitable for rapid profiling applications.
  • The approach provides a robust solution for mitigating the impact of pattern intensity aberrations in 3D metrology.