Abstract
In this paper, a non-supervised technique for on-line paper characterisation is presented. The method uses self-organising maps (SOM) and texture analysis for clustering different kinds of paper according to their properties. A light-through technique is used to get pictures of paper. Then, effective texture features are extracted from greyscale images and the dimensionality of the feature data is reduced with SOM allowing visual analysis of measurements. The method makes it possible to implicitly extract important information about paper formation. The approach provides excellent results. A classification error below 1% was achieved for four quality classes when local binary pattern (LBP) texture features were used. The improvement to the previously used texture features in paper inspection is huge: the classification error was reduced by over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view of the inspected data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Norman B, Wahren D (1974) The measurement of mass distribution in paper sheet using a beta radiographic method. Svensk Papperstidning 77(11):397–406
Cresson T, Luner P (1990) Characterization of paper formation. Part 2: the texture analysis of paper formation. Tappi J 73(12):175–184
Bernie JP, Douglas WJM (1996) Local grammage distribution and formation of paper by light transmission image analysis. Tappi J 79(1):193–202
Bouyndain M, Colom JF, Navarro R, Pladellorens J (2001) Determination of paper formation by Fourier analysis of light transmission images. Appita J 54(2):103–105, 115
Keller DS, Lewalle, Luner P (1999) Wavelet analysis of simulated paper formation. Pap Puu 81(7):499–505
Boyndain M, Colom JF, Pladellorens J (1999) Using wavelets to determine paper formation by light transmission image analysis. Tappi J 82(7):153–158
Jordan BD, Nguyen NG (1986) Specific perimeter – a graininess parameter for formation and print-mottle textures. Pap Puu 6(7):476–482
Niskanen M, Silvén O, Kauppinen H (2001) Color and texture based wood inspection with non-supervised clustering. In: SCIA 2001 – 12th Scandinavian conference of image analysis, Bergen, Norway, pp 336–342
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1):51–59
Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Ojala T, Valkealahti K, Oja E, Pietikäinen M (2001) Texture discrimination with multidimensional distributions of signed gray level differences. Pattern Recognition 34:727–739
Harwood D, Ojala T, Pietikäinen M, Kelman S, Davis LS (1995) Texture classification by center-symmetric auto-correlation, using kullback discrimination of distributions. Pattern Recognition Lett 16:1–10
Laws KL (1980) Textured image segmentation. PhD thesis, University of Southern California, Los Angeles, USA
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598
Kohonen T (1997) Self-organizing maps. Springer, Berlin Heidelberg New York
Niskanen M, Kauppinen H, Silvén O (2002) Real-time aspects of SOM-based visual surface inspection. Proc SPIE 4664:123–134
Acknowledgments
The financial support provided by the Academy of Finland is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Turtinen, M., Pietikäinen, M., Silvén, O. et al. Paper characterisation by texture using visualisation-based training. Int J Adv Manuf Technol 22, 890–898 (2003). https://doi.org/10.1007/s00170-003-1699-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-003-1699-6