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Efficient Background Segmentation and Seed Point Generation for a Single-Shot Stereo System.

Xiao Yang1,2, Xiaobo Chen3,4, Juntong Xi5,6,7

  • 1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. yangxiao1992@sjtu.edu.cn.

Sensors (Basel, Switzerland)
|December 2, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient stereo matching algorithm for single-shot 3D shape measurement. The new method improves background segmentation and seed point generation, enhancing measurement precision and speed.

Keywords:
background segmentationdigital image correlationseed point generationsingle-shot 3D measurement

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Metrology

Background:

  • Single-shot stereo 3D shape measurement offers noise robustness and rapid acquisition.
  • Efficient stereo matching is crucial, relying on effective background segmentation and seed point generation.

Purpose of the Study:

  • To propose a more efficient and automated stereo matching algorithm for 3D shape measurement.
  • To enhance the speed and accuracy of background segmentation and seed point generation.

Main Methods:

  • Digital Image Correlation (DIC) forms the basis of the proposed algorithm.
  • Background segmentation utilizes the standard deviation of image gradients and an adaptive threshold.
  • Scale-Invariant Feature Transform (SIFT)-based feature matching and 2D triangulation are used for seed point parameter estimation.

Main Results:

  • The algorithm achieves an average background segmentation time of 240 milliseconds for 1280 × 960 pixel images.
  • High efficiency in seed point generation was confirmed across various convergence criteria.
  • Experimental simulations and real tests validated the improved efficiency and precision.

Conclusions:

  • The proposed DIC-based stereo matching algorithm significantly enhances efficiency in 3D shape measurement.
  • The method provides a robust and automated solution for background segmentation and seed point generation.
  • This advancement contributes to faster and more precise single-shot stereo 3D measurement applications.