Appendix to: Computer vision and machine learning techniques for optical micrograph defect classification using a novel gray thresholding technique

This is supplementary material for https://doi.org/10.1007/s40192-026-00474-1.

This page contains two zip files with: * Classification Code -- Machine Learning pipeline for Optical Micrograph Defect Classification * Optimal Gray Threshold -- Novel method for finding threshold values in grayscale images for Optical Micrographs

Each ZIP contains code, data, and a README for specific implementations.

Citation

Hasbrouck, Christie; Miller, Simon; Martin, Jay (2026). Appendix to: Computer vision and machine learning techniques for optical micrograph defect classification using a novel gray thresholding technique [Data set]. Scholarsphere. https://doi.org/10.26207/83hq-wr53

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  • View: Optimal Gray Threshold Codes.zip

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Metadata

Work Title Appendix to: Computer vision and machine learning techniques for optical micrograph defect classification using a novel gray thresholding technique
Access
Open Access
Creators
  1. C. R. Hasbrouck
  2. Simon W. Miller
  3. Jay Martin
License MIT License
Work Type Software Or Program Code
Publication Date January 28, 2026
DOI doi:10.26207/83hq-wr53
Related URLs
Deposited January 28, 2026

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Version 1
published

  • Created
  • Updated
  • Added Creator C. R. Hasbrouck
  • Added Creator Simon W. Miller
  • Added Creator Jay Martin
  • Updated
  • Updated Description, Publication Date Show Changes
    Description
    • Codes
    Publication Date
    • 2026-01-30
  • Updated License Show Changes
    License
    • https://opensource.org/licenses/MIT
  • Added README.md
  • Updated
  • Deleted README.md
  • Added readme.md
  • Deleted readme.md
  • Added README.md
  • Added LICENSE
  • Updated Publication Date Show Changes
    Publication Date
    • 2026-01-30
    • 2026-01-28
  • Published
  • Updated Work Title Show Changes
    Work Title
    • Codes
    • Appendix to: Computer vision and machine learning techniques for optical micrograph defect classification using a novel gray thresholding technique
  • Updated Description Show Changes
    Description
    • Codes
    • This is supplementary material for (paper DOI once published).
    • This page contains two zip files with: * Classification Code -- Machine Learning pipeline for Optical Micrograph Defect Classification * Optimal Gray Threshold -- Novel method for finding threshold values in grayscale images for Optical Micrographs
    • Each ZIP contains code, data, and a README for specific implementations.

Version 2
published

  • Created
  • Updated Work Title Show Changes
    Work Title
    • Codes
    • Appendix to: Computer vision and machine learning techniques for optical micrograph defect classification using a novel gray thresholding technique
  • Updated Description Show Changes
    Description
    • Codes
    • This is supplementary material for (paper DOI once published).
    • This page contains two zip files with:
    • * Classification Code -- Machine Learning pipeline for Optical Micrograph Defect Classification
    • * Optimal Gray Threshold -- Novel method for finding threshold values in grayscale images for Optical Micrographs
    • Each ZIP contains code, data, and a README for specific implementations.
  • Deleted LICENSE
  • Deleted README.md
  • Added Optimal Gray Threshold Codes.zip
  • Added README.md
  • Added URL.txt
  • Added Classification Codes.zip
  • Deleted URL.txt
  • Published
  • Updated

Version 3
published

  • Created
  • Updated Description Show Changes
    Description
    • This is supplementary material for (paper DOI once published).
    • This is supplementary material for https://doi.org/10.1007/s40192-026-00474-1.
    • This page contains two zip files with:
    • * Classification Code -- Machine Learning pipeline for Optical Micrograph Defect Classification
    • * Optimal Gray Threshold -- Novel method for finding threshold values in grayscale images for Optical Micrographs
    • Each ZIP contains code, data, and a README for specific implementations.
  • Published
  • Updated
  • Updated Related URLs Show Changes
    Related URLs
    • https://doi.org/10.1007/s40192-026-00474-1