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Center-environment feature models for materials image segmentation based on machine learning.

Yuexing Han1,2, Ruiqi Li3, Shen Yang3

  • 1School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China. han_yx@i.shu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new machine learning method for analyzing material microstructures. The "center-environment segmentation" (CES) model improves image segmentation accuracy by considering pixel neighborhoods and domain knowledge.

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Area of Science:

  • Materials Science
  • Computer Vision
  • Machine Learning

Background:

  • Material properties are influenced by composition and microstructure.
  • Current analysis of complex microstructure images relies heavily on human expertise, lacking automated quantitative methods.
  • Machine learning offers a promising approach for intelligent identification of material phases.

Purpose of the Study:

  • To develop an automated quantitative characterization method for complex material microstructure images.
  • To propose a novel machine learning-based image segmentation model incorporating environmental features and domain knowledge.

Main Methods:

  • Development of the "center-environment segmentation" (CES) feature model for image segmentation.
  • CES model utilizes neighborhood information as features for each pixel, capturing spatial relationships.
  • An iterative integrated machine learning approach was employed for model training and correction.

Main Results:

  • Successful application of the CES model to segment seven diverse material images, including steels and woods.
  • The CES method demonstrated superior performance in determining boundary contours compared to conventional methods, particularly in steel image segmentation.
  • The iterative integration of domain knowledge and environment features significantly enhanced segmentation accuracy.

Conclusions:

  • The proposed CES model effectively segments complex material microstructures.
  • Incorporating domain knowledge and environmental features improves the accuracy of machine learning-based image segmentation.
  • This approach offers a robust solution for the quantitative characterization of material microstructures.