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Image Thresholding Based on Fuzzy Particle Swarm Optimization

  • Anderson Carlos Sousa SantosEmail author
  • Helio Pedrini
Chapter

Abstract

Segmentation is a crucial stage in the image analysis process, whose main purpose is to partition an image into meaningful regions of interest. Thresholding is the simplest image segmentation method, where a global or local threshold value is selected for segmenting pixels into background and foreground regions. However, the determination of a proper threshold value is typically dependent on subjective assumptions or empirical rules. In this work, we propose and analyze an image thresholding technique based on a fuzzy particle swarm optimization. Several images are used in our experiments to show the effectiveness of the developed approach.

Keywords

Image thresholding Particle swarm optimization Image segmentation Fuzzy threshold Fitness function 

Notes

Acknowledgements

The authors are thankful to São Paulo Research Foundation (grant FAPESP #2014/12236-1) and the Brazilian Council for Scientific and Technological Development (grant CNPq #305169/2015-7 and scholarship #141647/2017-5) for their financial support.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil

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