Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops

  • J. Jay Liu
  • John F. MacGregor
Original Paper


A new machine vision approach for quantitatively estimating and monitoring the appearance and aesthetics of manufactured products is presented. The approach is composed of three steps: (1) wavelet-based textural feature extraction from product images, (2) estimation of measures of the product appearance through subspace projection of the textural features, and (3) monitoring of the appearance in the latent variable subspace of the textural features. The methodology is specifically designed to treat the stochastic nature of the visual appearance of many manufactured products. This nondeterministic aspect of product appearance has been an obstacle for the success of machine vision in many industries. The emphasis of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This allows for the on-line monitoring and the eventual feedback control of product appearance. This approach is successfully applied to the estimation and monitoring of the aesthetic quality of manufactured stone countertops.


Engineered stone countertops Machine vision Monitoring Visual appearance Wavelet texture analysis Principal component analysis 


  1. 1.
    Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities, 2nd edn. Academic Press, San Diego, CA (1997)Google Scholar
  2. 2.
    Marshall, A.D., Martin, R.R.: Computer Vision, Models and Inspection. World Scientific Publishing, Singapore (1992)Google Scholar
  3. 3.
    Hyper Dictionary:±visionGoogle Scholar
  4. 4.
    Liu, J., MacGregor, J.F., Duchesne, C., Bartolacci, G.: Monitoring of flotation processes using multiresolutional multivariate image analysis. Miner. Eng. 18(1), 65–76 (2005)CrossRefGoogle Scholar
  5. 5.
    Liu, J., MacGregor, J.F.: Modeling and optimization of product appearance: application to injection-molded plastic panels. Ind. Eng. Chem. Res. 44, 4687–4696 (2005)CrossRefGoogle Scholar
  6. 6.
    Bharati, M., Liu, J., MacGregor, J.F.: Image texture analysis: methods and comparisons. Chemometrics Intell. Lab. Syst. 72(1), 57–71 (2004)CrossRefGoogle Scholar
  7. 7.
    Honglu, Y., MacGregor, J.F., Haarsma, G., Bourg, W.: Digital imaging for on-line monitoring and control of industrial snack food processes. Ind. Eng. Chem. Res. 42(13), 3036–3044 (2003)CrossRefGoogle Scholar
  8. 8.
    Honglu, Y., MacGregor, J.F.: Monitoring flames in an industrial boiler using multivariate image analysis. AIChE J. 50(7), 1474–1483 (2004)CrossRefGoogle Scholar
  9. 9.
    Liu, J., MacGregor, J.F.: On the extraction of spectral and spatial information for image analysis. Chemometrics Intell. Lab. Syst. (2004)Google Scholar
  10. 10.
    Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  11. 11.
    Rioul, O., Duhamel, P.: Fast algorithms for discrete and continuous wavelet transforms. IEEE Trans. Inform. Theory 38(2), 569–586 (1992)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Daubechies, I.: Ten lectures on wavelets. In: Proceedings of the CBMS-NSF Reg. Conf. Series in Applied Math. no. 61. SIAM, Philadelphia, PA (1992)Google Scholar
  13. 13.
    Vetterli, M., Kovaević, J.: Wavelets and Subband Coding. Prentice Hall, Englewood Cliffs, NJ (1995)zbMATHGoogle Scholar
  14. 14.
    Randen, T., Husoy, J.H.: Filtering for texture classification: a comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)CrossRefGoogle Scholar
  15. 15.
    Tuceryan, M., Jain, A.K.: Texture analysis. In: Chen, C.H., et al., (eds.) Handbook of Pattern Recognition and Computer Vision, 2nd edn., pp. 235–276. World Scientific Publishing, New Jersey (1993)Google Scholar
  16. 16.
    Chang, T., Kuo, C.C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–441 (1993)CrossRefGoogle Scholar
  17. 17.
    Etdmad, K., Chellappa, R.: Separability-based multiscale basis selection and feature extraction for signal and image classification. IEEE Trans. Image Process. 7(10), 1453–1465 (1998)CrossRefGoogle Scholar
  18. 18.
    Laine, A., Fan, J.: Texture classification by wavelet packet signatures. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1186–1191 (1993)CrossRefGoogle Scholar
  19. 19.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933)CrossRefGoogle Scholar
  20. 20.
    Karhunen, K.: Uber lineare methoden in der Wahrsccheilichkeitsrechnung. Ann. Acad. Sci. Fenn., Ser. A1: Math.-Phys. 37, 3–79 (1947)Google Scholar
  21. 21.
    Loéve, M.: Probability Theory. Van Nostrand, New York (1963)zbMATHGoogle Scholar
  22. 22.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(Part II), 179–188 (1936)Google Scholar
  23. 23.
    Bell, A.J., Sejnoski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995)PubMedCrossRefGoogle Scholar
  24. 24.
    Friedman, J.H., Tukey, J.W.: A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. 23(9), 881–890 (1974)zbMATHCrossRefGoogle Scholar
  25. 25.
    Kresta, J.V., MacGregor, J.F., Marlin, T.E.: Multivariate statistical monitoring of process operating performance. Can. J. Chem. Eng. 69, 35–47 (1991)CrossRefGoogle Scholar
  26. 26.
    Kourti, T., MacGregor, J.F.: Multivariate statistical process control methods for monitoring and diagnosing process and product performance. J. Qual. Technol. 28, 409–428 (1996)Google Scholar
  27. 27.
    Jackson, J.E.: A User's Guide to Principal Components. Wiley-Interscience, New York (1991)zbMATHCrossRefGoogle Scholar
  28. 28.
    Lee, J.-.M, Yoo, C., Lee, I.B.: Statistical process monitoring using independent component analysis. J. Process Control 14(5), 467–485 (2004)CrossRefGoogle Scholar
  29. 29.
    Mojsilović, A., Popović, M.V., Rackkov, D.M.: On the selection of an optimal wavelet basis for texture characterization. IEEE Trans. Image Process. 9(12), 2043–2050 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  30. 30.
    Besse, P., de Falguerolles, A.: Application of resampling methods to the choice of dimension in principal component analysis. In: Härdle, W., Simar, L. (eds.) Computer Intensive Methods in Statistics, pp. 167–174. Physica-Verlag, Heidelberg, Germany (1993)Google Scholar
  31. 31.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading, MA (1993)Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • J. Jay Liu
    • 1
    • 2
  • John F. MacGregor
    • 1
  1. 1.Department of Chemical EngineeringMcMaster UniversityHamiltonCanada
  2. 2.Samsung ElectronicsAsanKorea

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