Texture Vision: A View from Art Conservation

  • Pierre Vernhes
  • Paul Whitmore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5897)


The appreciation of many works of visual art derives from the observation and interpretation of the object surface. The visual perception of texture is key to interpreting those surfaces, for the texture provides cues about the nature of the material and the ways in which the artist has manipulated it to create the object. The quantification of texture can be undertaken in two ways: by recording the physical topography of the surface or by analyzing an image that accurately portrays the texture. For most art objects, this description of texture on a microscopic level is not very useful, since how those surface features are observed by viewers is not directly provided by the analysis. For this reason, image analysis seems a more promising approach, for in the images the surfaces will naturally tend to be rendered as they would when viewing the object. In this study, images of textured surfaces of prototype art objects are analyzed in order to identify the methods and the metrics that can accurately characterize slight changes in texture. Three main applications are illustrated: the effect of the conditions of illumination on perceived texture, the characterization of changes of object due to degradation, and the quantification of the efficiency of the restoration.


Elevation Angle Grazing Angle Texture Vision Azimuthal Position Weave Structure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tuceryan, M., Jain, A.K.: In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, pp. 207–248. World Scientific, Singapore (1998)Google Scholar
  2. 2.
    Jain, A., Robert, P., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)CrossRefGoogle Scholar
  3. 3.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Brooks/Cole, Pacific Grove (1998)Google Scholar
  4. 4.
    Motoyoshi, I., Nishida, S., Sharan, L., Adelson, E.H.: Image statistics and perception of surface qualities. Nature 447, 206–209 (2007)CrossRefGoogle Scholar
  5. 5.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  6. 6.
    Ngan, H.Y.T., Pang, G.K.H., Yung, S.P., Ng, M.K.: Wavelet based methods on patterned fabric defect detection. Int. J. Pattern Recogn 38, 559–576 (2005)CrossRefGoogle Scholar
  7. 7.
    Lin, H.C., Wang, L.L., Yang, S.N.: Regular-texture image retrieval based on texture-primitive extraction. Image Vis. Comput. 17, 51–63 (1999)CrossRefGoogle Scholar
  8. 8.
    Kuo, C.F.J., Su, T.L.: Gray relational analysis for recognizing fabric defects. Textile Res. J. 73, 461–465 (2003)CrossRefGoogle Scholar
  9. 9.
    Sandy, C., Norton-Wayne, L., Harwood, R.: The automated inspection of lace using machine vision. Mech. J. 5, 215–231 (1995)Google Scholar
  10. 10.
    Kumar, A., Pang, G.: Defect detection in textured materials using Gabor filters. IEEE Trans. Ind. Applicat. 38(2), 425–440 (2002)CrossRefGoogle Scholar
  11. 11.
    Baykal, I.C., Muscedere, R., Jullien, G.A.: On the use of hash functions for defect detection in textures for in-camera web inspection systems. In: Proc. IEEE Int. Symp. Circuits Systems, ISCAS, vol. 5, pp. 665–668 (2002)Google Scholar
  12. 12.
    Chetverikov, D.: Pattern regularity as a visual key. Image Vis. Comput. 18, 975–985 (2000)CrossRefGoogle Scholar
  13. 13.
    Chetverikov, D., Hanbury, A.: Finding defects in texture using regularity and local orientation. Pattern Recognit. 35, 2165–2180 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Esmay, F., Griffith, R.: An Investigation of Cleaning Methods for Untreated Wood. Postprints of the Wooden Artifacts Group of the American Institute for Conservation of Historic and Artistic Works, AIC, Washington, DC, pp. 56–64 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pierre Vernhes
    • 1
  • Paul Whitmore
    • 1
  1. 1.Art Conservation Research Center, Department of ChemistryCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations