Skip to main content
SpringerLink
Log in

Surface Texture Using Photometric Stereo Data: Classification and Direction of Illumination Detection

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

This paper generalizes the recently proposed sinusoidal model used for modeling the variation of texture features under changes in illumination direction, so that it can handle surfaces which are very rough and of variable albedo. It deals with the problem of identifying the direction of illumination of a rough surface from a single image, using information from a photometric stereo set of images. In addition, it presents methodology for classifying the texture of a rough surface, using generalized normals that capture both shape and albedo information. It assumes that the surface is Lambertian and is presented to the camera in a fronto-parallel view.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Barsky, S., Petrou, M.: The 4-source photometric stereo technique for 3-dimensional surfaces in the presence of highlights and shadows. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1239–1252 (2003)

    Article  Google Scholar 

  2. Chantler, M.J.: Why illuminant direction is fundamental to texture analysis. IEE Proc. Vis. Image Signal Process. 142(4), 199–206 (1995)

    Article  Google Scholar 

  3. Chantler, M.J., McGunnigle, G., Penirschke, A., Petrou, M.: Estimating lighting direction and classifying textures. In: Rosin, P.L., Marshall, D. (eds.) BMVC2002, British Machine Vision Conference, Cardiff, 2–5 September 2002, vol. 2, pp. 737–746 (2002), ISBN 1-901725-19-7

  4. Chantler, M.J., Schmidt, M., Petrou, M., McGunnigle, G.: The effect of illuminant rotation on texture filters: Lissajous’s ellipses. In: ECCV2002, European Conference on Computer Vision, August 2002, vol. III, pp. 289–303 (2002)

  5. Chantler, M.J., Petrou, M., Penirsche, A., Schmidt, M., McGunnigle, G.: Classifying surface texture while simultaneously estimating illumination direction. Int. J. Comput. Vis. 62, 83–96 (2005)

    Google Scholar 

  6. Coleman, E.N., Jain, R.: Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry. Comput. Graph. Image Process. 18, 309–328 (1982)

    Article  Google Scholar 

  7. Cula, O.G., Dana, K.J.: 3D texture recognition using bidirectional feature histograms. Int. J. Comput. Vis. 59(1), 33–60 (2004)

    Article  Google Scholar 

  8. Dana, K.J., Nayar, S.K.: Histogram model for 3D textures. In: IEEE Conference on Computer Vision and Pattern Recognition, June 1998, pp. 618–624 (1998)

  9. Dana, K.J., Nayar, S.K.: Correlation model for 3D textures. In: ICCV99: IEEE International Conference on Computer Vision, pp. 1061–1067 (1999)

  10. Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, June 1997, pp. 151–157 (1997)

  11. van Ginneken, B., Koenderink, J.J., Dana, K.J.: Texture histograms as a function of irradiation and viewing direction. Int. J. Comput. Vis. 31(2–3), 169–184 (1999)

    Article  Google Scholar 

  12. Healey, G., Wang, L.: Illumination-invariant recognition of texture in color images. J. Opt. Soc. Am. A 12(9), 1877–1883 (1995)

    Article  Google Scholar 

  13. Kay, G., Caelly, T.: Estimating the parameters of an illumination model using photometric stereo. Graph. Models Image Process. 57(5), 365–388 (1995)

    Article  Google Scholar 

  14. Koenderink, J.J., Pont, S.C.: Irradiation direction from texture. J. Opt. Soc. Am. A 20(10), 1875–1882 (2003)

    Article  Google Scholar 

  15. Koenderink, J.J., van Doorn, A.J., Kappers, A.M.L., te Pas, S.F., Pont, S.C.: Illumination direction from texture shading. J. Opt. Soc. Am. A 20(6), 987 (2003)

    Article  Google Scholar 

  16. Kube, P., Pentland, A.P.: On the imaging of fractal surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 704–707 (1988)

    Article  MATH  Google Scholar 

  17. Laws, K.I.: Textured image segmentation. PhD thesis, Electrical Engineering, University of Southern California (2003)

  18. Leung, T., Malik, J.: Representing and recognising the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 1(43), 26–44 (2001)

    Google Scholar 

  19. Liu, X., Yu, Y., Shum, H.-Y.: Synthesizing bidirectional texture functions for real-world surfaces. In: SIGGRAPH, pp. 97–106 (2001)

  20. Llado, X., Petrou, M., Marti, J.: Texture recognition by surface rendering. Opt. Eng. J. 44(3), 0370011 (2005)

    Google Scholar 

  21. Llado, X., Oliver, A., Petrou, M., Freixenet, J., Marti, J.: Simultaneous surface texture classification and illumination tilt angle prediction. In: Harvey, R., Bangham, J.A. (eds.) Proceedings of the British Machine Vision Conference, Norwich, England, 8–11 September 2003, pp. 789–798 (2003)

  22. Nayar, S.K., Ikeuchi, K., Kanade, T.: Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Trans. Robot. Autom. 6(4), 418–431 (1990)

    Article  Google Scholar 

  23. Penirschke, A., Chantler, M.J., Petrou, M.: Illuminant rotation invariant classification of 3D surface textures using Lissajous‘s ellipses. In: Texture2002, The 2nd International Workshop on Texture Analysis and Synthesis, June 2002, pp. 101–107 (2002)

  24. Smith, M.L.: The analysis of surface texture using photometric stereo acquisition and gradient space domain mapping. Image Vis. Comput. 17, 1009–1019 (1999)

    Article  Google Scholar 

  25. Smith, M.L., Hill, T., Smith, G.: Surface texture analysis based upon the visually acquired perturbation of surface normals. Image Vis. Comput. 15, 949–955 (1997)

    Article  Google Scholar 

  26. Smith, M.L.: Surface Inspection Techniques—Using the Integration of Innovative Machine Vision and Graphical Modelling Techniques. Professional Engineering Publishing (2000). ISBN 1-86058-292-3

  27. Suen, P., Healey, G.: The analysis and recognition of real-world textures in three dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 22(5), 491–503 (2000)

    Article  Google Scholar 

  28. Tagare, H.D., de Figueiredo, R.J.P.: A theory of photometric stereo for a class of diffuse non-Lambertian surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 133–152 (1991)

    Article  Google Scholar 

  29. Tong, X., Zhang, J., Liu, L., Wang, X., Guo, B., Shum, H.-Y.: Synthesizing bidirectional texture functions on arbitrary surfaces. In: SIGGRAPH, pp. 665–672 (2002)

  30. Varma, M., Zisserman, A.: Texture classification: are filter banks necessary? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2003, vol. 2, pp. 691–698 (2003)

  31. Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Opt. Eng. 19(1), 139–144 (1980)

    Google Scholar 

  32. Woodham, R.J., Iwahori, Y., Barman, R.A.: Photometric stereo: Lambertian reflectance and light sources with unknown direction and strength. Technical report, University of British Columbia Department of Computing Science (1991)

  33. http://www.macs.hw.ac.uk/texturelab/database/Photex/index.htm

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronic Engineering Department, Imperial College, London, SW7 2AZ, UK

    Svetlana Barsky & Maria Petrou

Authors
  1. Svetlana Barsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Petrou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Petrou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barsky, S., Petrou, M. Surface Texture Using Photometric Stereo Data: Classification and Direction of Illumination Detection. J Math Imaging Vis 29, 185–204 (2007). https://doi.org/10.1007/s10851-007-0031-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-007-0031-8

Keywords

Search

Navigation