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Guided fireworks algorithm applied to multilevel image thresholding

Guided fireworks algorithm applied to multilevel image thresholding

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Digital image segmentation is often used as a preprocessing step in image analysis where different objects in the image need to be separated. One of the most used techniques for image segmentation is multilevel thresholding. Different criteria are used for determination of optimal thresholds, but in all cases, multilevel thresholding is a hard optimization problem that cannot be solved by deterministic methods. Swarm intelligence stochastic optimization metaheuristics have been proven to be very successful for such problems. In this chapter, we propose adjusted guided fireworks algorithm (GFWA) for multiple optimal threshold determination based on Kapur's entropy, Otsu's criteria and Tsallis' entropy. Comparison with other state-of-the-art techniques shows that the GFWA exhibits superior performance.

Chapter Contents:

  • Abstract
  • 2.1 Introduction
  • 2.2 Literature review
  • 2.3 Multilevel digital image thresholding
  • 2.3.1 Kapur's thresholding method
  • 2.3.2 Otsu's thresholding method
  • 2.3.3 Thresholding based on Tsallis' entropy
  • 2.4 Guided fireworks algorithm for multilevel thresholding
  • 2.5 Simulation results
  • 2.6 Conclusion
  • Acknowledgment
  • References

Inspec keywords: image segmentation; optimisation; swarm intelligence; stochastic processes

Other keywords: multilevel thresholding; digital image segmentation; multiple optimal threshold determination; image analysis; hard optimization problem; swarm intelligence stochastic optimization metaheuristics; guided fireworks algorithm; image thresholding

Subjects: Optimisation techniques; Computer vision and image processing techniques; Optical, image and video signal processing; Optimisation techniques

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