Similarity Based Cross-Section Segmentation in Rough Log End Images

  • Rudolf Schraml
  • Andreas Uhl
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)


This work treats cross-section (CS) segmentation in digital images of rough wood log ends. Existing CS segmentation approaches are focused on computed tomography CS images of logs and no approach and experimental evaluation for digital images has been presented so far. Segmentation of cross-sections in rough log end images is a prerequisite for the development of novel log end analysis applications (e.g. biometric log recognition or automated log grading). We propose a simple and fast computable similarity-based region growing algorithm for CS segmentation. In our experiments we evaluate different texture features (Local binary patterns & Intensity histograms) and histogram distances. Results show that the algorithm achieves the most accurate results in combination with intensity histograms and the earth movers distance. Generally, we conclude that for certain applications simple texture features and a matured distance metric can outperform higher-order texture features and basic distance metrics.


Wood imaging Cross-section analysis Cross-section segmentation Rough log end images 


  1. 1.
    Wu, J., Liew, D.: A computer vision method for detection of external log cracks and pith in log cross-section images. In: Procs. of the World Automation Congress: International Symposium on Intelligent Automation and Control (ISIAC 2000), Hawaii, USA (2000)Google Scholar
  2. 2.
    Hanning, T., Kickingereder, R., Casasent, D.: Determining the average annual ring width on the front side of lumber. In: Osten, W., Kujawinska, M., Creath, K. (eds.) Proceedings of SPIE: Optical Measurement Systems for Industrial Inspection, Munich, Germany, vol. 5144, pp. 707–716 (2003)Google Scholar
  3. 3.
    Österberg, P., Ihalainen, H., Ritala, R.: Method for analyzing and classifying wood quality through local 2d spectrum of digital log end images. In: Proceedings of International Conference on Advanced Optical Diagnostics in Fluids, Tokyo, JP (2004)Google Scholar
  4. 4.
    Norell, K., Borgefors, G.: Estimation of pith position in untreated log ends in sawmill environments. Computers and Electronics in Agriculture 63, 155–167 (2008)CrossRefGoogle Scholar
  5. 5.
    Schraml, R., Uhl, A.: Pith estimation on rough log end images using local fourier spectrum analysis. In: Proceedings of the 14th Conference on Computer Graphics and Imaging (CGIM 2013), Innsbruck, AUT (2013)Google Scholar
  6. 6.
    Cerda, M., Hitschfeld-Kahler, N., Mery, D.: Robust Tree-Ring Detection. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS, vol. 4872, pp. 575–585. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Norell, K.: An automatic method for counting annual rings in noisy sawmill images. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 307–316. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Norell, K.: Counting annual rings on pinus sylvestris end faces in sawmill industry. Computers and Electronics in Agriculture 75, 231–237 (2010)CrossRefGoogle Scholar
  9. 9.
    Longuetaud, F., Mothe, F., Kerautret, B., Krähenbühl, A., Hory, L., Leban, J.M., Debled-Rennesson, I.: Automatic knot detection and measurements from x-ray ct images of wood: A review and validation of an improved algorithm on softwood samples. Computers and Electronics in Agriculture 85, 77–89 (2012)CrossRefGoogle Scholar
  10. 10.
    Schraml, R., Uhl, A.: Temporal and longitudinal variances in wood log cross-section image analysis. In: IEEE International Conference on Image Processing 2014 (ICIP 2014), Paris, FR (2014)Google Scholar
  11. 11.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV 2001), vol. 1, pp. 105–112 (2001)Google Scholar
  12. 12.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision 70, 109–131 (2006)CrossRefGoogle Scholar
  13. 13.
    Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10, 266–277 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Chan, T., Esedoglu, S., Ni, K.: Histogram based segmentation using Wasserstein distances. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 697–708. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  16. 16.
    Haindl, M., Mikeš, S.: Unsupervised texture segmentation using multiple segmenters strategy. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 210–219. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Unser, M.: Texture Classification and segmentation using wavelet frames. IEEE Transactions on Image Processing 4, 1549–1600 (1995)CrossRefGoogle Scholar
  18. 18.
    Wang, B., Zhang, L.: Supervised texture segmentation using wavelet transform. Proceedings of the International Conference on Neural Networks and Signal Processing 2, 1078–1082 (2003)Google Scholar
  19. 19.
    Weldon, T.P., Higgins, W.E., Dunn, D.F.: Efficient gabor filter design for texture segmentation. Pattern Recognition 29, 2005–2015 (1996)CrossRefGoogle Scholar
  20. 20.
    Eiterer, L., Facon, J., Menoti, D.: Postal envelope address block location by fractalbased approach. In: Proceedings of the 17th Brazilian Symposium on Computer Graphics and Image Processing, pp. 90–97 (2004)Google Scholar
  21. 21.
    Materka, A., Strzelecki, M.: Texture analysis methods - a review. Technical report, Institute of Electronics, Technical University of Lodz (1998)Google Scholar
  22. 22.
    de Oliveira Nunes, E., Conci, A.: Texture segmentation considering multiband, multiresolution and affine invariant roughness. In: XVI Brazilian Symposium on Computer Graphics and Image Processing, pp. 254–261 (2003)Google Scholar
  23. 23.
    Stitou, Y., Turcu, F., Berthoumieu, Y., Najim, M.: Three-dimensional textured image blocks model based on wold decomposition. IEEE Transactions on Signal Processing 55, 3247–3261 (2007)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Eckley, I.A., Nason, G.P., Treloar, R.L.: Locally stationary wavelet fields with application to the modelling and analysis of image texture. Journal of the Royal Statistical Society: Series C (Applied Statistics) 59, 595–616 (2010)MathSciNetGoogle Scholar
  25. 25.
    Atto, A., Berthoumieu, Y., Bolon, P.: 2-d wavelet packet spectrum for texture analysis. IEEE Transactions on Image Processing 22, 2495–2500 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Jung, M., Peyré, G., Cohen, L.D.: Texture segmentation via non-local non-parametric active contours. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds.) EMMCVPR 2011. LNCS, vol. 6819, pp. 74–88. Springer, Heidelberg (2011)Google Scholar
  27. 27.
    Jung, M., Peyré, G., Cohen, L.D.: Nonlocal active contours. SIAM Journal on Imaging Sciences 5, 1022–1054 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  29. 29.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  30. 30.
    Mäenpää, T.: The Local Binary Pattern Approach to Texture Analysis – Extensions and Applications. PhD thesis, University of Oulu (2003)Google Scholar
  31. 31.
    Rubner, Y., Tomasi, C., Guibas, L.: A metric for distributions with applications to image databases. In: Sixth International Conference on Computer Vision, pp. 59–66 (1998)Google Scholar
  32. 32.
    Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29, 551–559 (1983)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Rudolf Schraml
    • 1
  • Andreas Uhl
    • 1
  1. 1.University of SalzburgSalzburgAustria

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