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Thermal Image Segmentation Using Evolutionary Computation Techniques

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Advances in Soft Computing and Machine Learning in Image Processing

Part of the book series: Studies in Computational Intelligence ((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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Authors and Affiliations

  1. Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense de Madrid, Madrid, Spain

    Salvador Hinojosa & Gonzalo Pajares

  2. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Jalisco, Mexico

    Erik Cuevas & Noé Ortega-Sanchez

Authors
  1. Salvador Hinojosa
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  2. Gonzalo Pajares
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  3. Erik Cuevas
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  4. Noé Ortega-Sanchez
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Corresponding author

Correspondence to Salvador Hinojosa .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. CUCEI, Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico

    Diego Alberto Oliva

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Hinojosa, S., Pajares, G., Cuevas, E., Ortega-Sanchez, N. (2018). Thermal Image Segmentation Using Evolutionary Computation Techniques. In: Hassanien, A., Oliva, D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham. https://doi.org/10.1007/978-3-319-63754-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-63754-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63753-2

  • Online ISBN: 978-3-319-63754-9

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