Thermal Image Segmentation Using Evolutionary Computation Techniques

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


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).


  1. 1.
    Vadivambal, R., Jayas, D.S.: Applications of Thermal Imaging in Agriculture and Food Industry-A Review. Food Bioprocess Technol. 4, 186–199 (2011). doi: 10.1007/s11947-010-0333-5 CrossRefGoogle Scholar
  2. 2.
    Al-Kassir, A.R., Fernandez, J., Tinaut, F.V., Castro, F.: Thermographic study of energetic installations. Appl. Therm. Eng. 25, 183–190 (2005). doi: 10.1016/j.applthermaleng.2004.06.013 CrossRefGoogle Scholar
  3. 3.
    Leykin, A., Hammoud, R.: Pedestrian tracking by fusion of thermal-visible surveillance videos. Mach. Vis. Appl. 21, 587–595 (2010). doi: 10.1007/s00138-008-0176-5 CrossRefGoogle Scholar
  4. 4.
    Wei, W., Xia, R., Xiang, W., Hui, B., Chang, Z., Liu, Y., Zhang, Y.: Recognition of Airport Runways in FLIR Images Based on Knowledge. IEEE Geosci. Remote Sens. Lett. 11, 1534–1538 (2014). doi: 10.1109/LGRS.2014.2299898 CrossRefGoogle Scholar
  5. 5.
    Al-Obaidy, F., Yazdani, F., Mohammadi, F.A.: Intelligent testing for Arduino UNO based on thermal image. Comput. Electr. Eng. 58, 88–100 (2017). doi: 10.1016/j.compeleceng.2017.01.014 CrossRefGoogle Scholar
  6. 6.
    Pitarma, R., Crisóstomo, J., Jorge, L.: Analysis of Materials Emissivity Based on Image Software. Springer, Cham, pp. 749–757 (2016). doi: 10.1007/978-3-319-31232-3_70
  7. 7.
    Ring, E.F.J., Ammer, K., Ring, E.F.J.: Infrared thermal imaging in medicine. Physiol. Meas. 33, R33 (2012). doi: 10.1088/0967-3334/33/3/R33 CrossRefGoogle Scholar
  8. 8.
    Mehra, M., Bagri, A., Jiang, X., Ortiz, J.: Image analysis for identifying mosquito breeding grounds. In: 2016 IEEE International Conference on Sensing Communication and Networking SECON Work. IEEE, pp. 1–6 (2016). doi: 10.1109/SECONW.2016.7746808
  9. 9.
    Gade, R., Moeslund, T.B.: Thermal cameras and applications: A survey. Mach. Vis. Appl. 25, 245–262 (2014). doi: 10.1007/s00138-013-0570-5 CrossRefGoogle Scholar
  10. 10.
    Vollmer, M., Möllmann, K.-P.: Wiley InterScience (Online service), Infrared Thermal Imaging : Fundamentals, Research and Applications. Wiley-VCH (2010)Google Scholar
  11. 11.
    Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13, 3066–3091 (2013). doi: 10.1016/j.asoc.2012.03.072 CrossRefGoogle Scholar
  12. 12.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Gr. Image Process. 29, 273–285 (1985). doi: 10.1016/0734-189X(85)90125-2
  13. 13.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986). doi: 10.1016/0031-3203(86)90030-0 CrossRefGoogle Scholar
  14. 14.
    Otsu, N.: Threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. SMC-9, 62–66 (1979).
  15. 15.
    Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146 (2004). doi: 10.1117/1.1631315 CrossRefGoogle Scholar
  16. 16.
    Yin, P.-Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184, 503–513 (2007). doi: 10.1016/j.amc.2006.06.057 MathSciNetMATHGoogle Scholar
  17. 17.
    Horng, M.-H., Liou, R.-J.: Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst. Appl. 38, 14805–14811 (2011). doi: 10.1016/j.eswa.2011.05.069 CrossRefGoogle Scholar
  18. 18.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997). doi: 10.1109/4235.585893 CrossRefGoogle Scholar
  19. 19.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007). doi: 10.1007/s10898-007-9149-x MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(n.d.), 341–359. doi: 10.1023/A:1008202821328
  21. 21.
    Loganathan, G.V.V., Geem, Z.W., Kim, J.H., Loganathan, G.V.V.: A new heuristic optimization algorithm: harmony search. Simulation. 76, 60–68 (2001). doi: 10.1177/003754970107600201 CrossRefGoogle Scholar
  22. 22.
    Yang, X.S.: Flower pollination algorithm for global optimization. Lecture Notes Computer Science (Including Subser. Lecture Notes Artificial Intelligence Lecture Notes Bioinformatics), vol. 7445 pp. 240–249, LNCS (2012). doi: 10.1007/978-3-642-32894-7_27
  23. 23.
    Mantegna, R.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. E. (1994). 20 Oct 2015
  24. 24.
    Zukal, M., Mekyska, J., Cika, P., Smekal, Z.: Interest points as a focus measure in multi-spectral imaging. Radioengineering. 22, 68–81 (2013). doi: 10.1109/TSP.2012.6256402 Google Scholar
  25. 25.
    Silva, A., Saade, L.F., Sequeiros, D.C.M., Silva, G.O., Paiva, A.C., Bravo, A.C., Conci, R.S.: A new database for breast research with infrared image. J. Med. Imaging Heal. Inform. 4, 92–100 (2014)Google Scholar
  26. 26.
    Davis, J., Keck, M.: A two-stage approach to person detection in thermal imagery. IEEE Work. Appl. Comput. Vis. (2005)Google Scholar
  27. 27.
    Ghamisi, P., Couceiro, M.S., Benediktsson, J.A., Ferreira, N.M.F.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39, 12407–12417 (2012). doi: 10.1016/j.eswa.2012.04.078 CrossRefGoogle Scholar
  28. 28.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004). doi: 10.1109/TIP.2003.819861 CrossRefGoogle Scholar
  29. 29.
    Agrawal, S., Panda, R., Bhuyan, S., Panigrahi, B.K.: Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol. Comput. 11, 16–30 (2013). doi: 10.1016/j.swevo.2013.02.001 CrossRefGoogle Scholar
  30. 30.
    Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011). doi: 10.1109/TIP.2011.2109730 MathSciNetCrossRefGoogle Scholar

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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