Thermal Image Segmentation Using Evolutionary Computation Techniques

  • Salvador Hinojosa
  • Gonzalo Pajares
  • Erik Cuevas
  • Noé Ortega-Sanchez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 730)

Abstract

This chapter analyzes the performance of selected evolutionary computation techniques (ECT) applied to the segmentation of forward looking infrared (FLIR) images. FLIR images arise challenges for classical image processing techniques since the capture devices usually generate low-resolution images prone to noise and blurry outlines. Traditional ECTs such as artificial bee colony (ABC), differential evolution (DE), harmony search (HS), and the recently published flower pollination algorithm (FPA) are implemented and evaluated using as objective function the between-class variance (Otsu’s method) and the Kapur’s entropy. The comparison pays particular attention to the quality of the segmented image by evaluating three specific metrics named peak-to-signal noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Salvador Hinojosa
    • 1
  • Gonzalo Pajares
    • 1
  • Erik Cuevas
    • 2
  • Noé Ortega-Sanchez
    • 2
  1. 1.Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad InformáticaUniversidad Complutense de MadridMadridSpain
  2. 2.Departamento de ElectrónicaUniversidad de Guadalajara, CUCEIJaliscoMexico

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