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Butterfly Effect in Chaotic Image Segmentation.

Radu Mărginean1, Anca Andreica1,2, Laura Dioşan1,2

  • 1IMOGEN Research Institute, County Clinical Emergency Hospital, 400006 Cluj-Napoca, Romania.

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|December 8, 2020
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Summary
This summary is machine-generated.

This study enhances the GrowCut algorithm using cellular automata and chaotic features for image segmentation. Small changes in initial conditions can lead to significant improvements in segmentation outcomes, demonstrating a butterfly effect.

Keywords:
butterfly effectcellular automatacomplex networksemergent phenomenaimage segmentation

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Area of Science:

  • Computational modeling
  • Image processing
  • Complexity science

Background:

  • Complex systems in nature and artificial environments offer features to enhance computational models.
  • Cellular automata simulate complexity through simple, local interactions to produce global behavior.
  • Image segmentation is a key task in computational modeling where performance can be improved.

Purpose of the Study:

  • To utilize cellular automata for simulating complexity in image segmentation.
  • To enhance the classical GrowCut algorithm with chaotic features.
  • To investigate the emergence of the butterfly effect in cellular automata for image segmentation.

Main Methods:

  • A modified GrowCut algorithm, termed Band-Based GrowCut, was developed.
  • This enhanced algorithm incorporates chaotic features and an extended, stochastic neighborhood.
  • Numerical experiments were conducted on synthetic and natural images to evaluate performance.

Main Results:

  • The Band-Based GrowCut algorithm demonstrated improved image segmentation performance, achieving a Dice coefficient of 71% for 2D images.
  • The study confirmed the presence of the butterfly effect when perturbing the cellular automaton's neighborhood system.
  • Results indicate that minor alterations in initial conditions can cause substantial changes in segmentation results.

Conclusions:

  • Enhancing cellular automata with chaotic features and stochastic neighborhoods improves image segmentation.
  • The butterfly effect is observable in this enhanced GrowCut model, linking local interactions to global outcomes.
  • This approach offers a novel method for improving computational model performance through complexity simulation.