Speckled Images Segmentation and Algorithm Comparison

Luigi Cinque, Rossella Cossu, Rosa Maria Spitaleri

Abstract

An image segmentation process, based on the level set method, consists in the time evolution of an initial curve until it reaches the boundary of the objects to be extracted. Classically the evolution of the initial curve is determined by a speed function. In this paper, the speed in the level set procedure is characterized by the combination of two different speed functions and the resulting algorithm is applied to speckled images, like SAR (Synthetic Aperture Radar) images. In order to assess improvements of the segmentation performance, the computational process is tested on synthetic and then applied to real images. Performances are evaluated on synthetic images by using the Hausdorff distance. The real SAR images were acquired during the ERS2 mission.

References

  1. Cinque, L. and Cossu, R. (2011). Region segmentation from SAR images Lectures Notes Computer Science LNCS 6978 (ICIAP 2011), SPRINGER.
  2. Sethian, J. A. (1999) Level set methods and fast marching methods, Cambridge University Press, 1999.
  3. Sethian, J. A. (2001) Evolution, implementation and application of level set and fast marching methods for advancing front, Journal of Computational Physics, 169 (2001).
  4. Osher, S. and Fedkiw, R. (2002) Level set methods and dynamic implicit surfaces, Springer-Verlag New York, 2002.
  5. Ben Ayed, I. Mitiche, A. and Belhadj, Z. (2005), Multiregion level-set partitioning of synthetic aperture radar images, IEEE Trans. Pattern Analysis and Machine Intelligence, 27 (2005).
  6. Mitiche, A. and Ben Ayed, I. (2011), Variational and level set methods in image segmentation, (2011) SPRINGER.
  7. Ben Salah, M. Ben Ayed, I. and Mitiche, A. (2012), Active curve recovery of region boundary patterns, IEEE Trans. Pattern Analysis and Machine Intelligence, 34 (2012).
  8. Yongjian, Yu and Acton Scott, T.(2002), Speckle reducing anisotropic diffusion, IEEE Trans. on Image Processing, 11 (2002).
  9. Huttenlocher, D. Klanderman, G. and Rucklidge W. (1993) Comparing Images Using the Hausdorff Distance, IEEE Trans. Pattern Analysis Machine Intelligence,15 (1993).
  10. Mumford, D. and Shah, J. (1989), Optimal approximations by piecewise smooth functions and associated variational problems, Comm. Pure Appl.Math., 42 (1989).
  11. Spitaleri, R. M. March, R. and Arena D. (1999), Finite difference solution of Euler equation arising in variational image segmentation, Numerical Algorithms, 21 (1999).
  12. Cinque, L. Cossu, R. and Spitaleri, R.M. (2014), Applied variational SAR image segmentation, MASCOT12 & ISGG12 Proceedings, IMACS Series in Computational and Applied Mathematics,18 (2014).
  13. Chan, T. F. and Vese, L. A. (2001), Active Contours without edge, IEEE Trans. on Image Processing, 10 (2001).
  14. Kass, M. Witkin, A. and Terzopoulos D. (2001), Snakes: active contour models, Journal of Computational Vision, 1 (1988).
  15. Cerimele, M.M. Cinque, L. and Cossu, R. (2009), Coastline detection from SAR images by level set model, Lectures Notes Computer Science LNCS 5716 (ICIAP 2009), SPRINGER.
  16. Perona, P. and Malik, J.,(1990) Scale space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Analysis and Machine Intelligence, 12 (1990).
  17. Li, C. Xu, C. Gui and Fox, D. (2010), Distance regularized level set evolution and its application to image segmentation, IEEE Trans. on Image Processing, 11 (2010).
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Paper Citation


in Harvard Style

Cinque L., Cossu R. and Maria Spitaleri R. (2015). Speckled Images Segmentation and Algorithm Comparison . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 111-118. DOI: 10.5220/0005166901110118


in Bibtex Style

@conference{icpram15,
author={Luigi Cinque and Rossella Cossu and Rosa Maria Spitaleri},
title={Speckled Images Segmentation and Algorithm Comparison},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={111-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005166901110118},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Speckled Images Segmentation and Algorithm Comparison
SN - 978-989-758-077-2
AU - Cinque L.
AU - Cossu R.
AU - Maria Spitaleri R.
PY - 2015
SP - 111
EP - 118
DO - 10.5220/0005166901110118