Nothing Special   »   [go: up one dir, main page]

Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3753))

Abstract

In this paper we propose a novel type of scales-spaces which is emerging from the family of inhomogeneous pseudodifferential equations \((I - \tau\Delta)^{\frac{t}{2}}u\) with τ ≥ 0 and scale parameter t ≥ 0. Since they are connected to the convolution semi-group of Bessel potentials we call the associated operators {R \(^{n}_{t,{ \tau}}\) | 0≤ τ,t} either Bessel scale-space (τ=1), R \(^{n}_{t}\) for short, or scaled Bessel scale-space (τ ≠1). This is the first concrete example of a family of scale-spaces that is not originating from a PDE of parabolic type and where the Fourier transforms \(\mathcal{F}(R^n_{t,\tau})\) do not have exponential form. These properties make them different from other scale-spaces considered so far in the literature in this field.

In contrast to the α-scale-spaces the integral kernels for R \(^{n}_{t,{\tau}}\) can be given in explicit form for any t, τ ≥ 0 involving the modified Bessel functions of third kind K ν . In theoretical investigations and numerical experiments on 1D and 2D data we compare this new scale-space with the classical Gaussian one.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abramowitz, M., Stegun, I.A. (eds.): Handbook of Mathematical Functions, 9th edn. Dover Publications, Inc., New York (1972)

    MATH  Google Scholar 

  2. Alvarez, L., Guichard, F., Lions, P.-L., Morel, J.-M.: Axioms and fundamental equations in image processing. Archive for Rational Mechanics and Analysis 123, 199–257 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  3. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Applied Mathematical Sciences, vol. 147. Springer, New York (2002)

    MATH  Google Scholar 

  4. Babaud, J., Witkin, A.P., Baudin, M., Duda, R.O.: Uniqueness of the Gaussian kernel for scale space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 26–33 (1986)

    Article  MATH  Google Scholar 

  5. Bauer, H.: Wahrscheinlichkeitstheorie. Walter de Gruyter, Berlin (1991)

    MATH  Google Scholar 

  6. Brockett, R.W., Maragos, P.: Evolution equations for continuous-scale morphological filtering. IEEE Transactions on Signal Processing 42, 3377–3386 (1994)

    Article  Google Scholar 

  7. Burgeth, B., Didas, S., Weickert, J.: Relativistic scale-spaces. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 1–12. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Cao, F.: Geometric Curve Evolution and Image Processing. Lecture Notes in Mathematics, vol. 1805. Springer, Berlin (2003)

    MATH  Google Scholar 

  9. Donoghue, W.F.: Distributions and Fourier Tarnsforms. Academic Press, New York (1969)

    Google Scholar 

  10. Duits, R., Florack, L., de Graaf, J., ter Haar Romeny, B.: On the axioms of scale space theory. JMIV 20(3), 267–298 (2004)

    Article  MathSciNet  Google Scholar 

  11. Felsberg, M., Sommer, G.: Scale-adaptive filtering derived from the Laplace equation. In: Radig, B., Florczyk, S. (eds.) Dagstuhl Seminar 2000. LNCS, vol. 2032, pp. 95–106. Springer, Berlin (2001)

    Google Scholar 

  12. Florack, L.: Image Structure. Computational Imaging and Vision, vol. 10. Kluwer, Dordrecht (1997)

    Google Scholar 

  13. Gilboa, G., Sochen, N.A., Zeevi, Y.Y.: Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Transactions on Image Processing 11(7), 689–703 (2002)

    Article  Google Scholar 

  14. Hao, P., Zhang, C., Dang, A.: Co-histogram and image degradation evaluation. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 195–203. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Heijmans, H.J.A.M.: Scale-spaces, PDEs and scale-invariance. In: Kerckhove, M. (ed.) Scale-Space 2001. LNCS, vol. 2106, pp. 215–226. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  16. Hille, E., Philips, R.S.: Functional Analysis and Semi-Groups. American Mathematical Society, Providence (1957)

    Google Scholar 

  17. Iijima, T.: Basic theory of pattern observation. Papers of Technical Group on Automata and Automatic Control. IECE, Japan (December 1959) (In Japanese)

    Google Scholar 

  18. Iijima, T.: Basic theory on the construction of figure space. Systems, Computers, Controls 2(5), 51–57 (1971) (In English)

    MathSciNet  Google Scholar 

  19. Jackway, P.T., Deriche, M.: Scale-space properties of the multiscale morphological dilation–erosion. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 38–51 (1996)

    Article  Google Scholar 

  20. Kimmel, R.: Numerical Geometry of Images: Theory, Algorithms, and Applications. Springer, New York (2003)

    Google Scholar 

  21. Lindeberg, T.: Scale-Space Theory in Computer Vision. Kluwer, Boston (1994)

    Google Scholar 

  22. Lindeberg, T.: On the axiomatic formulations of linear scale-space. In: Sporring, J., Nielsen, M., Florack, L., Johansen, P. (eds.) Gaussian Scale-Space Theory. Computational Imaging and Vision, vol. 8, pp. 75–97. Kluwer, Dordrecht (1997)

    Google Scholar 

  23. Aronszajn, N., Smith, K.T.: Theory of bessel potentials. Ann. Inst. Fourier (Grenoble) 11, 385–475 (1961)

    MATH  MathSciNet  Google Scholar 

  24. Olver, P.J., Sapiro, G., Tannenbaum, A.: Classification and uniqueness of invariant geometric flows. Comptes Rendus de l’Académie des Sciences de Paris, Série I 319, 339–344 (1994)

    MATH  MathSciNet  Google Scholar 

  25. Pauwels, E.J., Van Gool, L.J., Fiddelaers, P., Moons, T.: An extended class of scale-invariant and recursive scale space filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 691–701 (1995)

    Article  Google Scholar 

  26. Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)

    Article  Google Scholar 

  27. Salden, A.H.: Dynamic Scale-Space Paradigms. PhD thesis, Faculty of Medicine, Utrecht University, The Netherlands (November 1996)

    Google Scholar 

  28. Sapiro, G., Tannenbaum, A.: Affine invariant scale-space. International Journal of Computer Vision 11, 25–44 (1993)

    Article  Google Scholar 

  29. Sporring, J., Nielsen, M., Florack, L., Johansen, P. (eds.): Gaussian Scale-Space Theory. Computational Imaging and Vision, vol. 8. Kluwer, Dordrecht (1997)

    MATH  Google Scholar 

  30. Taylor, M.E.: Partial Differential Equations I – Basic Theory. Springer, New York (1996)

    Google Scholar 

  31. ter Haar Romeny, B.M. (ed.): Geometry-Driven Diffusion in Computer Vision. Computational Imaging and Vision, vol. 1. Kluwer, Dordrecht (1994)

    MATH  Google Scholar 

  32. van den Boomgaard, R.: The morphological equivalent of the Gauss convolution. Nieuw Archief Voor Wiskunde 10(3), 219–236 (1992)

    MATH  MathSciNet  Google Scholar 

  33. Weickert, J.: Anisotropic Diffusion in Image Processing. Teubner, Stuttgart (1998)

    Google Scholar 

  34. Weickert, J., Ishikawa, S., Imiya, A.: Linear scale-space has first been proposed in Japan. Journal of Mathematical Imaging and Vision 10(3), 237–252 (1999)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Burgeth, B., Didas, S., Weickert, J. (2005). The Bessel Scale-Space. In: Fogh Olsen, O., Florack, L., Kuijper, A. (eds) Deep Structure, Singularities, and Computer Vision. DSSCV 2005. Lecture Notes in Computer Science, vol 3753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577812_8

Download citation

  • DOI: https://doi.org/10.1007/11577812_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29836-6

  • Online ISBN: 978-3-540-32097-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics