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

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Book cover Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (AI2SD 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 911))

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Abstract

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.

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References

  1. Nasir, F.A., et al.: A study of image processing in agriculture application under high performance computing environment (2012)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  8. Bayram, U., Can, G., Duzgun, S., Yalabik, N.: Evaluation of textural features for multispectral images, p. 81800I (2011)

    Google Scholar 

  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. 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). https://doi.org/10.1016/j.prosdent.2015.07.001. Published online 26 Sep 2015

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 5767521

    Google Scholar 

  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). https://doi.org/10.1109/iccic.2013.6724171

  15. Sagheb, E.: A Survey of Multithreading Image Analysis (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Optique and Photonic Team, Faculty of Sciences, M’hannech II, Tetuan, Morocco

    Jamal Bouchti & Adel Asselman

  2. Systems of Communications and Detection Laboratory, Faculty of Sciences, M’hannech II, Tetuan, Morocco

    Abdellah El Hajjaji

Authors
  1. Jamal Bouchti
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  2. Adel Asselman
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  3. Abdellah El Hajjaji
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Corresponding author

Correspondence to Jamal Bouchti .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Souani Tangier, Morocco

    Mostafa Ezziyyani

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Bouchti, J., Asselman, A., El Hajjaji, A. (2019). A Method for Segmentation of Agricultural Fields on Aerial Images with Markov Random Field Model. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-11878-5_11

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