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Nonparametric edge detection in speckled imagery

Author

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  • Girón, Edwin
  • Frery, Alejandro C.
  • Cribari-Neto, Francisco

Abstract

We address the issue of edge detection in Synthetic Aperture Radar imagery. In particular, we propose nonparametric methods for edge detection, and numerically compare them to an alternative method that has been recently proposed in the literature. Our results show that some of the proposed methods display superior results and are computationally simpler than the existing method. An application to real (not simulated) data is presented and discussed.

Suggested Citation

  • Girón, Edwin & Frery, Alejandro C. & Cribari-Neto, Francisco, 2012. "Nonparametric edge detection in speckled imagery," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(11), pages 2182-2198.
    • Handle: RePEc:eee:matcom:v:82:y:2012:i:11:p:2182-2198
    DOI: 10.1016/j.matcom.2012.04.013
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    References listed on IDEAS

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    1. Pianto, Donald M. & Cribari-Neto, Francisco, 2011. "Dealing with monotone likelihood in a model for speckled data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1394-1409, March.
    2. Lim, Dong Hoon, 2006. "Robust edge detection in noisy images," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 803-812, February.
    3. Cribari-Neto, Francisco & Frery, Alejandro C. & Silva, Michel F., 2002. "Improved estimation of clutter properties in speckled imagery," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 801-824, October.
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    Citations

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    Cited by:

    1. Chan Debora & Rey Andrea & Gambini Juliana & Frery Alejandro C., 2018. "Sampling from the 𝒢I distribution," Monte Carlo Methods and Applications, De Gruyter, vol. 24(4), pages 271-287, December.

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