Advertisement

Rough Surface Estimation Using the Kirchhoff Model

  • Hossein Ragheb
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

In this paper, we exploit the theory of light scattering from rough surfaces to estimate surface characteristics through reflectance measurements. Here, we analyse the Beckmann formulation of the Kirchhoff theory We then suggest two classes of surfaces for which the appropriate techniques can be used for estimating the surface roughness, the correlation length and the surface slope. Finally we show how the Beckmann model can be fitted to reflectance data for materials with very-rough surfaces. Since the Kirchhoff theory is inadequate for large angles of incidence, we make use of a modification to the Beckmann model. The proposed techniques have significant potential in computer vision for texture model acquisition and realistic reflectance modelling.

Keywords

Incidence Angle Correlation Length Photometric Stereo Small Incidence Angle Specular Direction 
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.

References

  1. 1.
    P. Beckmann and A. Spizzochino, The Scattering of Electromagnetic Waves from Rough Surfaces, Pergamon, New York, 1963.zbMATHGoogle Scholar
  2. 2.
    H.E. Bennett and J. Porteus, “Relation Between Surface Roughness and Specular Reflectance at Normal Incidence,” JOSA, vol. 51, no. 2, pp. 123–129, 1961.MathSciNetGoogle Scholar
  3. 3.
    J.M. Bennett and L. Mattsson, Introduction to Surface Roughness and Scattering, Optical Society of America, Washington, D.C., pp. 38–56, 1989.Google Scholar
  4. 4.
    J. Caron, J. Lafait and C. Andraud, “Scalar Kirchhoff’s Model for Light Scattering from Dielectric Random Rough Surfaces,” Optics Communications, vol. 207, pp. 17–28, 2002.CrossRefGoogle Scholar
  5. 5.
    M. Chantler, M. Schmidt, M. Petrou and G. McGunnigle, “The Effect of Illuminant Rotation on Texture Filters: Lissajous’s Ellipses,” ECCV, vol. 3, pp. 289–303.Google Scholar
  6. 6.
    K.J. Dana and S.K. Nayar, “Histogram Model for 3D Textures,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 618–624, 1998.Google Scholar
  7. 7.
    K.J. Dana and S.K. Nayar, “Correlation Model for 3D Texture,” Proc. IEEE Int’l. Conf. on Computer Vision, pp. 1061–1067, 1999.Google Scholar
  8. 8.
    B.V. Ginneken, M. Stavridi and J.J. Koenderink, “Diffuse and Specular Reflectance from Rough Surfaces,” Applied Optics, vol. 37, no. 1, pp. 130–139, 1998.CrossRefGoogle Scholar
  9. 9.
    S.K. Nayar, K. Ikeuchi and T. Kanade, “Surface Reflection: Physical and Geometrical Perspectives,” IEEE Trans. PAMI, vol. 13, no. 7, pp. 611–634, 1991.Google Scholar
  10. 10.
    J.A. Ogilvy, Theory of Wave Scattering from Random Rough Surfaces, Adam Hilger, Bristol, 1991.zbMATHGoogle Scholar
  11. 11.
    K.E. Torrance and E.M. Sparrow, “Theory for Off-Specular Reflection from Roughened Surfaces,” J. of Optical Society of America, vol. 57, pp. 1105–1114, 1967.CrossRefGoogle Scholar
  12. 12.
    C.L. Vernold and J.E. Harvey, “A Modified Beckmann-Kirchoff Scattering Theory for Non-paraxial Angles,” Scattering and Surface Roughness, Proc. of the SPIE, vol. 3426, pp. 51–56, 1998.Google Scholar
  13. 13.
    L.B. Wolff, S.K. Nayar and M. Oren, “Improved Diffuse Reflection Models for Computer Vision,” Int’l. J. of Computer Vision, vol. 30, no. 1, pp. 55–71, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hossein Ragheb
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

Personalised recommendations