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A statistical model for contours in images.

François Destrempes1, Max Mignotte

  • 1Département d'Informatique et de Recherche Opérationnelle, C.P. 6128, Succ. Centre-Ville, Montréal, Quebec, Canada, H3C 3J7. destremp@iro.unmontreal.ca

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 6, 2004
PubMed
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This study introduces a novel unsupervised statistical model for image edge detection. Validated on 160 images, the method accurately identifies contours and shapes using advanced algorithms.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Accurate image analysis requires robust methods for edge and contour detection.
  • Existing techniques often lack unsupervised capabilities or require extensive parameter tuning.

Purpose of the Study:

  • To develop a novel unsupervised statistical model for image edge detection.
  • To present a global constrained Markov model for image contours.
  • To validate the model's efficacy through extensive experimentation.

Main Methods:

  • A statistical model for the gradient vector field of gray levels in images.
  • A global constrained Markov model utilizing the statistical model for likelihood.
  • Parameter estimation via Iterative Conditional Estimation (ICE).

Related Experiment Videos

  • Segmentation using Simulated Annealing (SA), Iterated Conditional Modes (ICM), or Modes of Posterior Marginals (MPM) Monte Carlo (MC) algorithms.

    • Main Results:

    • The proposed model provides an original unsupervised statistical method for edge detection with three variants.
    • Extensive testing on 160 images validated the model and its estimation procedures.
    • The model is suitable for applications requiring an energy term based on the log-likelihood ratio.

    Conclusions:

    • The developed statistical model and its associated algorithms offer a valid and effective approach to unsupervised edge detection.
    • The model demonstrates versatility, applicable to contour extraction, shape localization, and non-photo-realistic rendering.
    • This method provides a robust statistical likelihood for contour-based image analysis problems.