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Image Filtering Driven by Level Curves

  • Ajit Rajwade
  • Arunava Banerjee
  • Anand Rangarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5681)

Abstract

This paper presents an approach to image filtering that is driven by the properties of the iso-valued level curves of the image and their relationship with one another. We explore the relationship of our algorithm to existing probabilistically driven filtering methods such as those based on kernel density estimation, local-mode finding and mean-shift. Extensive experimental results on filtering gray-scale images, color images, gray-scale video and chromaticity fields are presented. In contrast to existing probabilistic methods, in our approach, the selection of the parameter that prevents diffusion across the edge is robustly decoupled from the smoothing of the density itself. Furthermore, our method is observed to produce better filtering results for the same settings of parameters for the filter window size and the edge definition.

Keywords

Color Image Gaussian Kernel Kernel Density Estimation Image Filter Level Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on PAMI 12(7), 629–639 (1990)CrossRefGoogle Scholar
  2. 2.
    Tschumperle, D., Deriche, R.: Vector-valued image regularization with pdes: A common framework for different applications. IEEE Trans. on PAMI 27(4), 506–517 (2005)CrossRefGoogle Scholar
  3. 3.
    Tang, B., Sapiro, G.: Color image enhancement via chromaticity diffusion. IEEE Trans. on Image Processing 10, 701–707 (1999)CrossRefzbMATHGoogle Scholar
  4. 4.
    Saint-Marc, P., Chen, J., Medioni, G.: Adaptive smoothing: a general tool for early vision. IEEE Trans. on PAMI 13(6), 514–520 (1991)CrossRefGoogle Scholar
  5. 5.
    Plataniotis, K., Venetsanopoulos, A.: Color image processing and applications. Springer, New York (2000)CrossRefGoogle Scholar
  6. 6.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846 (1998)Google Scholar
  7. 7.
    Cheng, Y.: Mean shift, mode seeking and clustering. IEEE Trans. on PAMI 17(8), 790–799 (1995)CrossRefGoogle Scholar
  8. 8.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. on PAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  9. 9.
    Chan, T., Shen, J.: Image Processing and Analysis: Variational, PDE, wavelets, and stochastic methods. SIAM, Philadelphia (2005)CrossRefzbMATHGoogle Scholar
  10. 10.
    Hadjidemetriou, E., Grossberg, M., Nayar, S.: Histogram preserving image transformations. IJCV 45(1), 5–23 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Rajwade, A., Banerjee, A., Rangarajan, A.: Probability density estimation using isocontours and isosurfaces: applications to information-theoretic image registration. IEEE Trans. on PAMI 31(3), 475–491 (2009)CrossRefGoogle Scholar
  12. 12.
    Barash, D., Comaniciu, D.: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. IVC 22, 73–81 (2004)CrossRefGoogle Scholar
  13. 13.
    Buades, A., Coll, B., Morel, J.M.: Neighborhood filters and PDEs. Numerische Mathematik 105(1), 1–34 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Subbarao, R., Meer, P.: Discontinuity preserving filtering over analytic manifolds. In: CVPR (2007)Google Scholar
  15. 15.
    van de Weijer, J., van den Bloomgard, R.: Local mode filtering. In: CVPR, vol. 2, pp. 428–436 (2001)Google Scholar
  16. 16.
    Sochen, N., Kimmel, R., Malladi, R.: A general framework for low level vision. IEEE Trans. on Image Processing 7, 310–318 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Trans. on PAMI 25, 281–288 (2003)CrossRefGoogle Scholar
  18. 18.
    Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: ICCV, pp. 438–445 (2001)Google Scholar
  19. 19.
    Awate, S., Whitaker, R.: Unsupervised, information-theoretic, adaptive image filtering for image restoration. IEEE Trans. on PAMI 28(3), 364–376 (2006)CrossRefGoogle Scholar
  20. 20.
    Buades, A., Coll, B., Morel, J.M.: Nonlocal image and movie denoising. IJCV 76(2), 123–139 (2008)CrossRefGoogle Scholar
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ajit Rajwade
    • 1
  • Arunava Banerjee
    • 1
  • Anand Rangarajan
    • 1
  1. 1.Department of CISEUniversity of FloridaGainesvilleUSA

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