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A bond percolation-based model for image segmentation.

I Hussain1, T R Reed

  • 1Gen. DataComm. Inc., Middlebury, CT.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study introduces a new bond percolation method to define Gibbs-Markov model parameters for image analysis. This approach enables multiresolution image segmentation by creating multiscale descriptions of these parameters.

Area of Science:

  • Computer Vision
  • Statistical Physics
  • Image Processing

Background:

  • Gibbs-Markov models are widely used for image segmentation.
  • Determining clique potential parameters is crucial for model accuracy.
  • Existing methods may lack multiscale capabilities.

Purpose of the Study:

  • To develop a novel bond percolation-based approach for determining Gibbs-Markov model clique potential parameters.
  • To establish a multiscale description of these parameters using renormalization group transformations.
  • To apply the derived parameters for multiresolution image segmentation.

Main Methods:

  • Utilizing bond percolation theory to define clique potential parameters.
  • Applying renormalization group transformations for multiscale analysis.

Related Experiment Videos

  • Implementing the multiscale parameters for image segmentation.

    • Main Results:

    • Successfully determined clique potential parameters as a function of local image characteristics.
    • Generated a multiscale description of the clique potential parameters.
    • Achieved multiresolution image segmentation using the novel approach.

    Conclusions:

    • The bond percolation-based method provides an effective way to parameterize Gibbs-Markov models.
    • The renormalization group transformation enables multiscale analysis for improved segmentation.
    • This work offers a novel framework for multiresolution image segmentation.