A Method for Segmentation of Agricultural Fields on Aerial Images with Markov Random Field Model

  • Jamal BouchtiEmail author
  • Adel Asselman
  • Abdellah El Hajjaji
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 911)


Aerial imaging has become important to areas like remote sensing, surveying, and particularly in the agricultural application areas. In this paper, we suggest an aerial image segmentation approach based on Markov random field model and Gibbs distributions, we introduce iterative algorithm process to minimize an energy function which incorporate a local characteristics of pixel like color and also Neighborhood characteristics like texture and CIEDE2000.


Aerial image segmentation Markov random field Gibbs distribution Potential energy function Iterative algorithm CIEDE2000 


  1. 1.
    Nasir, F.A., et al.: A study of image processing in agriculture application under high performance computing environment (2012)Google Scholar
  2. 2.
    Janwale, A.: Digital image processing applications in agriculture: a survey. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5, 622 (2015)Google Scholar
  3. 3.
    Verma, K., Singh, B.K., Thokec, A.S.: An enhancement in adaptive median filter for edge preservation. In: International Conference on Intelligent Computing, Communication & Convergence (ICCC-2015) (2015)CrossRefGoogle Scholar
  4. 4.
    Carron, T., Lambert, P.: Color edge detector using jointly hue, saturation and intensity. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 1994, vol. 3, pp. 977–981 (1994)Google Scholar
  5. 5.
    El Asnaoui, K., Aksasse, B., Ouanan, M.: Content-based color image retrieval based on the 2-D histogram and statistical moments. In: Second World Conference on Complex Systems (WCCS), Agadir, pp. 653–656 (2014)Google Scholar
  6. 6.
    Sural, S., Qian, G., Pramanik, S.: Segmentation and histogram generation using the HSV color space for image retrieval. In: Proceedings of the International Conference on Image Processing, vol. 2, p. II (2002)Google Scholar
  7. 7.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973). Scholar
  8. 8.
    Bayram, U., Can, G., Duzgun, S., Yalabik, N.: Evaluation of textural features for multispectral images, p. 81800I (2011)Google Scholar
  9. 9.
    Guerrout, E.-H., Mahiou, R., Ait-Aoudia, S.: Medical image segmentation on a cluster of PCs using Markov random fields. Int. J. New Comput. Archit. Appl. (IJNCAA) 3(1), 35–44 (2013)Google Scholar
  10. 10.
    Gómez-Polo, C., Muñoz, M.P., Luengo, M.C.L., Vicente, P., Galindo, P., Casado, A.M.M.: Comparison of the CIELab and CIEDE2000 color difference formulas. J. Prosthet. Dent. 115(1), 65–70 (2016). Published online 26 Sep 2015CrossRefGoogle Scholar
  11. 11.
    Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30, 21–30 (2005)CrossRefGoogle Scholar
  12. 12.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)CrossRefGoogle Scholar
  13. 13.
    Cruz, H., Eckert, M., Meneses, J.M., Martínez, J.F.: Fast evaluation of segmentation quality with parallel computing. Sci. Program. 2017, 9 (2017). Article ID 5767521Google Scholar
  14. 14.
    Dey, N., Mukherjee, A., Madhulika, Chakraborty, S., Samanta, S.: Parallel image segmentation using multi-threading and k-means algorithm. In: IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2013 (2013).
  15. 15.
    Sagheb, E.: A Survey of Multithreading Image Analysis (2015)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Optique and Photonic Team, Faculty of SciencesM’hannech IITetuanMorocco
  2. 2.Systems of Communications and Detection Laboratory, Faculty of SciencesM’hannech IITetuanMorocco

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