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A new criterion for automatic multilevel thresholding.

J C Yen1, F J Chang, S Chang

  • 1Inst. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This summary is machine-generated.

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A novel criterion for multilevel thresholding automatically determines optimal thresholds by minimizing a cost function. This method balances image discrepancy and data compression, offering an efficient alternative to existing techniques.

Area of Science:

  • Image processing
  • Computer vision
  • Digital signal processing

Background:

  • Image thresholding is crucial for image segmentation and analysis.
  • Existing multilevel thresholding methods often involve complex computations or suboptimal results.
  • Efficient and accurate multilevel thresholding remains an active research area.

Purpose of the Study:

  • To propose a new criterion for multilevel thresholding.
  • To develop an automated method for determining optimal threshold values.
  • To enhance the efficiency and effectiveness of image thresholding algorithms.

Main Methods:

  • A new criterion based on image discrepancy and bit representation is introduced.
  • A cost function integrating these two factors is formulated for multilevel thresholding.

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  • Minimization of the cost function allows automatic determination of threshold values.
  • The criterion builds upon a novel maximum correlation criterion for bilevel thresholding.

    • Main Results:

    • The proposed cost function is proven to have a unique minimum under mild conditions.
    • Computational analysis shows fewer mathematical operations compared to the maximum entropy criterion.
    • Simulation results demonstrate the effectiveness of the proposed multilevel thresholding method.

    Conclusions:

    • The new criterion provides an effective and computationally efficient approach to multilevel thresholding.
    • The method automatically determines optimal thresholds, balancing image fidelity and data compression.
    • This approach offers a significant improvement over existing methods, particularly in terms of computational cost.