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Image thresholding segmentation based on weighted Parzen-window and linear programming techniques.

Fusong Xiong1,2, Zhiqiang Zhang3,4, Yun Ling3,4

  • 1Soochow College, Soochow University, Suzhou, 215006, Jiangsu, China. xiongfusong@suda.edu.cn.

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Summary

This study introduces a novel image segmentation method using weighted Parzen-window and linear programming for accurate bi-level thresholding. The new approach demonstrates superior segmentation accuracy and robustness compared to existing techniques.

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Image segmentation by thresholding is a core task in image processing and computer vision.
  • Existing methods have limitations in accuracy and robustness.

Purpose of the Study:

  • To propose a new bi-level thresholding method for image segmentation.
  • To enhance segmentation accuracy and robustness.

Main Methods:

  • Utilized a weighted Parzen-window to analyze gray level distribution.
  • Formulated the thresholding problem as a linear programming problem.
  • Computed weighted Parzen-window coefficients for segmentation.

Main Results:

  • Achieved higher segmentation accuracy on synthetic, NDT, and benchmark images.
  • Demonstrated improved robustness compared to Otsu's (OTSU), Kapur's (KSW), CHPSO, GLLV, and GABOR methods.
  • Effectively identified foreground and background image boundaries.

Conclusions:

  • The proposed weighted Parzen-window and linear programming method offers superior performance for image thresholding.
  • This technique provides a robust and accurate solution for image segmentation tasks.