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Abstract

This paper is concerned with amoeba median filtering, a structure-adaptive morphological image filter. It has been introduced by Lerallut et al. in a discrete formulation. Experimental evidence shows that iterated amoeba median filtering leads to segmentation-like results that are similar to those obtained by self-snakes, an image filter based on a partial differential equation. We investigate this correspondence by analysing a space-continuous formulation of iterated median filtering. We prove that in the limit of vanishing radius of the structuring elements, iterated amoeba median filtering indeed approximates a partial differential equation related to self-snakes and the well-known (mean) curvature motion equation. We present experiments with discrete iterated amoeba median filtering that confirm qualitative and quantitative predictions of our analysis.

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References

  1. Alvarez, L., Lions, P.-L., Morel, J.-M.: Image selective smoothing and edge detection by nonlinear diffusion. II. SIAM Journal on Numerical Analysis 29, 845–866 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  2. Barash, D.: Bilateral filtering and anisotropic diffusion: towards a unified viewpoint. In: Kerckhove, M. (ed.) Scale-Space 2001. LNCS, vol. 2106, pp. 273–280. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  3. Caselles, V., Morel, J.-M., Sbert, C.: An axiomatic approach to image interpolation. IEEE Trans. Image Proc. 7(3), 376–386 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chui, C.K., Wang, J.: PDE models associated with the bilateral filter. Advances in Computational Mathematics (2008)

    Google Scholar 

  5. Didas, S., Weickert, J.: Combining curvature motion and edge-preserving denoising. In: Sgallari, F., Murli, F., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 568–579. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Dougherty, E.R., Astola, J. (eds.): Nonlinear Filters for Image Processing. SPIE Press, Bellingham (1999)

    MATH  Google Scholar 

  7. Guichard, F., Morel, J.-M.: Partial differential equations and image iterative filtering. In: Duff, I.S., Watson, G.A. (eds.) The State of the Art in Numerical Analysis, pp. 525–562. Clarendon Press, Oxford (1997)

    Google Scholar 

  8. Kimmel, R., Sochen, N., Malladi, R.: Images as embedding maps and minimal surfaces: movies, color, and volumetric medical images. In: Proc. 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 350–355 (1997)

    Google Scholar 

  9. Klette, R., Zamperoni, P.: Handbook of Image Processing Operators. Wiley, New York (1996)

    MATH  Google Scholar 

  10. Kuwahara, M., Hachimura, K., Eiho, S., Kinoshita, M.: Processing of RI-angiocardiographic images. In: Preston, J.K., Onoe, M. (eds.) Digital Processing of Biomedical Images, pp. 187–202. Plenum, New York (1976)

    Chapter  Google Scholar 

  11. Lerallut, R., Decencière, E., Meyer, F.: Image processing using morphological amoebas. In: Ronse, C., Najman, L., Decencière, E. (eds.) Mathematical Morphology: 40 Years On, pp. 13–22. Springer, Dordrecht (2005)

    Chapter  Google Scholar 

  12. Lerallut, R., Decencière, E., Meyer, F.: Image filtering using morphological amoebas. Image and Vision Computing 25(4), 395–404 (2007)

    Article  Google Scholar 

  13. Nagao, M., Matsuyama, T.: Edge preserving smoothing. Computer Graphics and Image Processing 9(4), 394–407 (1979)

    Article  Google Scholar 

  14. Sapiro, G.: Vector (self) snakes: a geometric framework for color, texture and multiscale image segmentation. In: Proc. IEEE International Conference on Image Processing 1996, vol. 1, pp. 817–820 (1996)

    Google Scholar 

  15. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. Sixth International Conference on Computer Vision, pp. 839–846. Narosa Publishing House (1998)

    Google Scholar 

  16. Tukey, J.W.: Exploratory Data Analysis. Addison–Wesley, Menlo Park (1971)

    MATH  Google Scholar 

  17. Van den Boomgaard, R.: Decomposition of the Kuwahara–Nagao operator in terms of linear smoothing and morphological sharpening. In: Mathematical Morphology: Proc. Sixth International Symposium, pp. 283–292. CSIRO Publishing (2002)

    Google Scholar 

  18. Whitaker, R.T., Xue, X.: Variable-conductance, level-set curvature for image denoising. In: Proc. IEEE International Conference on Image Processing 2001, pp. 142–145 (2001)

    Google Scholar 

  19. Yezzi Jr., A.: Modified curvature motion for image smoothing and enhancement. IEEE Trans. Image Proc. 7(3), 345–352 (1998)

    Article  MathSciNet  Google Scholar 

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Welk, M., Breuß, M., Vogel, O. (2009). Differential Equations for Morphological Amoebas. In: Wilkinson, M.H.F., Roerdink, J.B.T.M. (eds) Mathematical Morphology and Its Application to Signal and Image Processing. ISMM 2009. Lecture Notes in Computer Science, vol 5720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03613-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-03613-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03612-5

  • Online ISBN: 978-3-642-03613-2

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