Image Processing of Natural Calamity Images Using Healthy Bacteria Foraging Optimization Algorithm

  • P. Lakshmi Devi
  • S. Varadarajan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


The digital Image processing has emerged as an effective tool for analyzing the digital images of various fields and applications of engineering. Threshold technique is the most useful and well known among segmentation methods because of its robustness, simplicity, and high precision. This paper is an attempt to make an efficient segmentation Natural calamity images by Healthy Bacteria Foraging Optimization Algorithm.


Healthy bacterial foraging optimization Natural disasters Image segmentation Thresholding 


  1. 1.
    Hu, X., Shen, J., Shan, J., Pan, L.: Local edge distributions for detection of salient structure textures and objects. IEEE Geosci. Remote Sens. Lett. 10(3), 446–450 (2013)CrossRefGoogle Scholar
  2. 2.
    Zhang, L., Yang, K.: Region-of-interest extraction based on frequency domain analysis and silent region detection for remote sensing image. IEEE Geosci. Remote Sens. Lett. 11(5), 916–920 (2014)CrossRefGoogle Scholar
  3. 3.
    Lang, F., Yang, J., Li, D., Zhao, L., Shi, L.: Polari metric SAR image segmentation using statistical region merging. IEEE Geosci. Remote Sens. Lett. 11(2), 509–513 (2014)CrossRefGoogle Scholar
  4. 4.
    Zhang, L., Li, H., Wang, P., Yu, X.: Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial sub sampling visual attention model. GIsci. Remote Sens. 50(1), 112–132 (2013)MathSciNetGoogle Scholar
  5. 5.
    Rosenfeld, A., Kak, A.: Digital Picture Processing, vol. 2. Academic Press, New York (1982)Google Scholar
  6. 6.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.L.: Color image segmentation: advances and prospects. Pattern Recogn. 34(12), 2259–2281 (2011)CrossRefGoogle Scholar
  7. 7.
    Beenu, S.K.: Image segmentation using improved bacterial foraging algorithm. Int. J. Sci. Res. (IJSR) (2013)Google Scholar
  8. 8.
    Borji, A., Hamidi, M., Moghadam, A.M.E.: CLPSO-based fuzzy color image segmentation. In: Proceedings of the North American Fuzzy Information Processing Society, pp. 508–513 (2007)Google Scholar
  9. 9.
    Sowmya, B. Sheelarani, B.: Color image segmentation using soft computing techniques. Int. J. Soft Comput. Appl. 4, 69–80 (2009)Google Scholar
  10. 10.
    Dasgupta, S., Das, S.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Stud. Comput. Intell. 203, 23–55 (2009)Google Scholar
  11. 11.
    Zareh, S., Seyedjavadi, H.H., Erfani, H.: Grid scheduling using cooperative BFO algorithm. Am. J. Sci. Res. (62), 78–87 (2012). ISSN:1450-223XGoogle Scholar
  12. 12.
    Passino, K.M.: Biomimicry for Optimization, Control, Automation. Springer, London (2005)Google Scholar
  13. 13.
    Bakwad, K.M, Pattnaik, S.S., Sohi, B.S., Devi, S., Panigrahi, B.K., Sastri, G.S.V.R.: Bacterial foraging optimization technique cascaded with adaptive filter to enhance peak signal to noise ratio from single image. IETE J. Res. 55(4), (2009)Google Scholar
  14. 14.
    Liu, W., Chen, H., Chen, H., Chen, M.: RFID network scheduling using an adaptive bacteria foraging algorithm. J. Comput. Inf. Syst. (JCIS) 7(4), 1238–1245 (2011)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAnnamacharya Institute of Technology and SciencesRajampetIndia
  2. 2.Department of Electronics and Communication EngineeringSri Venkateswara University College of EngineeringTirupatiIndia

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