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Summary

This study introduces an efficient global binarization algorithm to address challenges in industrial imaging caused by uneven illumination. The method effectively removes illumination interference, improving image segmentation and reducing computational costs.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Image binarization is crucial for industrial applications but challenging due to uneven illumination.
  • Existing methods often yield unsatisfactory results in non-uniform lighting conditions.

Purpose of the Study:

  • To propose an efficient global binarization algorithm for images with nonuniform illumination.
  • To effectively eliminate uneven illumination interference for improved image segmentation.

Main Methods:

  • Estimating the inhomogeneous background surface using principal components in Gaussian scale space (GSS).
  • Employing a difference operator to extract foreground information.
  • Applying a global thresholding algorithm (e.g., Otsu method) for final binarization.

Main Results:

  • The proposed algorithm effectively eliminates uneven illumination interference.
  • Experimental results demonstrate promising binarization outcomes compared to classical methods.
  • The algorithm achieves low computational costs.

Conclusions:

  • The developed algorithm offers an effective solution for binarizing images under nonuniform illumination.
  • It provides a valuable tool for industrial image processing tasks requiring robust segmentation.