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Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution.

Qingyu Deng1, Zeyi Shi1, Congjie Ou1

  • 1Department of Physics, College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.

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Summary

A new image thresholding algorithm combines entropy and variance methods for better object recognition. This self-adaptive approach enhances target extraction from complex backgrounds, improving accuracy and robustness.

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Otsu-based algorithmgray-level distributionimage thresholdingnonextensive entropyself-adaptive algorithm

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Automatic object recognition requires effective segmentation algorithms to separate targets from backgrounds.
  • Image thresholding is a common technique due to its efficiency and simplicity.
  • Existing entropy-based and variance-based methods have limitations with diverse image types.

Purpose of the Study:

  • To develop a novel, self-adaptive image thresholding algorithm.
  • To combine the strengths of entropy-based and variance-based methods.
  • To enhance object recognition by improving target extraction from varied backgrounds.

Main Methods:

  • A new algorithm integrating entropy-based and variance-based thresholding is proposed.
  • The algorithm incorporates a nonextensive parameter to account for long-range pixel correlations.
  • Performance is evaluated against established methods using quantitative quality indices.

Main Results:

  • The proposed algorithm demonstrates superior performance in correctness and robustness.
  • Quantitative analysis using ME, RAE, MHD, and PSNR indices validates the algorithm's effectiveness.
  • The method successfully handles a more general scope of images compared to existing techniques.

Conclusions:

  • The developed self-adaptive image thresholding algorithm offers improved accuracy and robustness.
  • This approach shows significant potential for applications in self-adaptive object recognition.
  • Combining complementary thresholding techniques leads to more versatile image segmentation.