Surface Material Segmentation Using Polarisation

  • Nitya Subramaniam
  • Edwin Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

This paper describes the use of polarisation information for surface segmentation based on material characteristics. We work with both polarised and unpolarised light, and hence domains where the polarisation is either specular or diffuse. We commence by using moments to estimate the components of the polarisation image (mean-intensity, polarisation degree and phase) from images obtained through multiple polariser orientations. From the Fresnel theory, the phase of light remitted from a surface is equal to the azimuth angle of the remitted direction, and for materials with restricted ranges of refractive index the polarisation degree determines the zenith angle. Based on this observation, we parameterise the angular distribution of the mean intensity for remitted light using spherical harmonics. We explore how vectors of spherical harmonics can be used to characterise varying surface reflectance distributions, and segment a scene into different material patches using Mahalanobis distances and normalized graph cuts.

Keywords

Zenith Angle Mahalanobis Distance Azimuth Angle Polarise Light Polarisation Degree 
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.
    Atkinson, G., Hancock, E.R.: Recovery of surface orientation from diffuse polarization. IEEE Transactions on image Processing 15(6), 1653–1664 (2006)CrossRefGoogle Scholar
  2. 2.
    Atkinson, G., Hancock, E.R.: Polarization-based surface reconstruction via patch matching. Computer Analysis of Images and Patterns, 466–473 (2007)Google Scholar
  3. 3.
    Atkinson, G., Hancock, E.R.: Two-dimensional brdf estimation from polarisation. Computer Vision and Image Understanding 111(2), 126–141 (2008)CrossRefGoogle Scholar
  4. 4.
    Born, M., Wolf, E.: Principles of Optics, 7th (expanded) edn. Cambridge University Press, Cambridge (1999)CrossRefGoogle Scholar
  5. 5.
    Chung, M.K., Dalton, K.M., Davidson, R.J.: Tensor-based cortical surface morphometry via weighted spherical harmonic representation. IEEE Transactions on Medical Imaging 27(8), 1143–1151 (2008)CrossRefGoogle Scholar
  6. 6.
    Hecht, E.: Optics, 4th edn. Addison-Wesley, Reading (2002)Google Scholar
  7. 7.
    Jones, B.F., Fairney, P.T.: Recognition of shiny dielectric objects by analyzing the polarization of reflected light. Image and Vision Computing Journal 7 (1989)Google Scholar
  8. 8.
    Meriaudeau, F., Ferraton, M., Stolz, C., Morel, O., Bigué, L.: Polarization imaging for industrial inspection, vol. 6813 (2008)Google Scholar
  9. 9.
    Miyazaki, D., Kagesawa, M., Ikeuchi, K.: Transparent surface modeling from a pair of polarization images. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 73–82 (2004)CrossRefGoogle Scholar
  10. 10.
    Morel, O., Stolz, C., Meriaudeau, F., Gorria, P.: Active lighting applied to three-dimensional reconstruction of specular metallic surfaces by polarization imaging. Applied Optics 45(17), 4062–4068 (2006)CrossRefGoogle Scholar
  11. 11.
    Rahmann, S., Canterakis, N.: Reconstruction of specular surfaces using polarization imaging. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 149–155 (2001)Google Scholar
  12. 12.
    Saupe, D., Vranić, D.V.: 3-d model retrieval with spherical harmonics and moments. In: Proceedings of the 23rd DAGM-Symposium on Pattern Recognition, pp. 392–397. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Shen, L., Ford, J., Makedon, F., Saykin, A.: A surface-based approach for classification of 3d neuroanatomic structures. Intelligent Data Analysis 8(6), 519–545 (2004)Google Scholar
  14. 14.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  15. 15.
    Sun, G., Onoichenco, E., Fu, Y., Liu, Y., Amell, R., McCandless, C., Reddy, R., Kumar, G., Guest, M.: High-throughput polarization imaging for defocus and dose inspection for production wafers, vol. 6518 (2007)Google Scholar
  16. 16.
    Wolff, L.B.: Polarization vision: a new sensory approach to image understanding. Image and Vision Computing 15, 81–93 (1997)CrossRefGoogle Scholar
  17. 17.
    Wolff, L.B., Boult, T.E.: Constraining object features using a polarisation reflectance model. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(7), 635–657 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nitya Subramaniam
    • 1
  • Edwin Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUnited Kingdom

Personalised recommendations