Image segmentation by flocking-like particle dynamics

Image segmentation by flocking-like particle dynamics

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In this chapter, we present a segmentation technique based on patterns that emerge from a system of moving particles. It is inspired from the flocking formation that can be frequently observed in nature, e.g. in large groups of birds and fish, which display a complex, coordinated motion without the guidance of any leader. First, we down-sample the image by dividing it into superpixels, each of which will be represented by a moving particle. After assigning random directions of motion for every particle, the dynamical evolution starts; after some iterations, it is expected that the new particle arrangement reflects useful image features, i.e. the segments we search for. We also present a comprehensive parameter analysis by objectively measuring our results compared to human-annotated ground-truth images. A comparison to some other segmentation algorithms is also performed, what enables us to discuss some pros and cons of our flocking-like approach. Rather than just present a new segmentation algorithm, our intention is to bring ideas that may motivate new studies and applications of self-organising dynamical systems to solve complex problems that involve large datasets.

Chapter Contents:

  • Abstract
  • 9.1 Introduction
  • 9.2 Related work
  • 9.3 Model description
  • 9.4 Experimental results
  • 9.4.1 Overview of achievable results
  • 9.4.2 Parameter analysis
  • 9.4.3 Comparative results
  • 9.5 Conclusions
  • Acknowledgement
  • References

Inspec keywords: image segmentation; image sampling

Other keywords: self-organising dynamical systems; flocking formation; complex problems; moving particle; human-annotated ground-truth images; complex motion; image segmentation; superpixels; comprehensive parameter analysis; dynamical evolution; flocking-like particle dynamics; segmentation algorithm; particle arrangement; coordinated motion; image features

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

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