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A minimum entropy approach to adaptive image polygonization.

Lothar Hermes1, Joachim M Buhmann

  • 1Dept. of Comput. Sci. III, Rheinische Friedrich-Wilhelms-Univ., Bonn, Germany. hermes@cs.unibonn.de

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study presents an adaptive image segmentation algorithm using polygonal segments and hierarchical triangulation. It effectively segments noisy images, avoiding overfitting with an information-theoretic stopping criterion.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Image segmentation is crucial for image analysis.
  • Existing methods may struggle with noisy data or overfitting.
  • A need exists for adaptive and efficient segmentation techniques.

Purpose of the Study:

  • Introduce a novel adaptive image segmentation algorithm.
  • Represent images using polygonal segments and hierarchical triangulation.
  • Develop a robust method for noisy imagery.

Main Methods:

  • Utilizes an intuitive generative model for pixel intensities.
  • Employs a hierarchical triangulation algorithm for cost function optimization.
  • Applies an information-theoretic bound for statistical significance and overfitting prevention.

Related Experiment Videos

Main Results:

  • The algorithm extracts compact descriptions of image structure via refined triangular meshes.
  • An information-theoretic bound serves as an effective stopping criterion.
  • A multiscale variant significantly improves computational efficiency.

Conclusions:

  • The proposed algorithm offers effective segmentation for noisy images.
  • It provides a compact representation of essential image structures.
  • Applications include contextual classification, remote sensing, and object recognition.