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On the Relationship between Variational Level Set-Based and SOM-Based Active Contours.

Mohammed M Abdelsamea1, Giorgio Gnecco2, Mohamed Medhat Gaber3

  • 1Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.

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Summary
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This survey reviews active contour models (ACMs) for image segmentation, focusing on variational level set and Self-Organizing Map (SOM) based approaches. It highlights how SOMs can improve contour evolution and avoid local minima in image analysis.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Segmentation

Background:

  • Active Contour Models (ACMs) optimize functionals for image segmentation.
  • Variational level set methods handle complex shapes and topological changes.
  • Self-Organizing Maps (SOMs) offer a machine learning approach to control contour evolution.

Purpose of the Study:

  • To survey and compare variational level set-based ACMs and SOM-based ACMs.
  • To analyze the relationship between these two ACM approaches.
  • To review state-of-the-art models from a machine learning perspective, detailing strengths and weaknesses.

Main Methods:

  • Review of variational level set methods for active contours.
  • Review of Self-Organizing Map (SOM) based active contour models.
  • Comparative analysis focusing on machine learning aspects.

Main Results:

  • SOM-based ACMs leverage topology preservation to learn edge information.
  • SOMs can overcome limitations of traditional ACMs, such as local minima entrapment.
  • The survey provides a comprehensive overview of model development and performance.

Conclusions:

  • Both variational level set and SOM-based ACMs are significant for image segmentation.
  • SOM-based ACMs present a promising machine learning-driven alternative for robust image analysis.
  • Understanding their strengths and weaknesses is crucial for selecting appropriate models.