Advertisement

European Journal of Wood and Wood Products

, Volume 76, Issue 5, pp 1379–1389 | Cite as

Quantitative image analysis of acoustic tomography in woods

  • Jorge Renato Andrade Strobel
  • Marco Antonio Garcia de Carvalho
  • Raquel Gonçalves
  • Cinthya Bertoldo Pedroso
  • Mariana Nagle dos Reis
  • Paulo S. Martins
Original
  • 128 Downloads

Abstract

The development of acoustic techniques for wood analysis through tomography has enabled the generation of images by means of nondestructive techniques. These images allow for the evaluation of the internal condition of wood trunks. This type of evaluation provides valuable information since the internal defects (e.g. holes) in the wood are difficult to identify—especially in its early stages of development. Whereas there is a substantial body of work that aims to improve these images by applying new interpolation and inspection techniques, the assessment of these techniques has traditionally been carried out via a bare visual analysis or inspection of the real wood trunk. In this work, an approach is proposed to quantitatively assess interpolation methods regarding their ability to correctly detect faults in the wood. This approach is based on a confusion matrix that allows for the computation of accuracy, reliability and recall. An experiment is presented using images from the cross-section of wood trunks generated by two interpolation methods applied for internal-hole detection: (1) an interpolation method using surrounding points and (2) the Ellipse Based Spatial Interpolation. The results demonstrated the effectiveness of the approach in quantitatively assessing and comparing these methods.

Notes

References

  1. Bond L, Saffari M (1984) Mode-conversion ultrasonic testing. In: Sharpe RS (ed) Nondestructive testing, vol 7, pp 145–189Google Scholar
  2. Bourke P (1992) Intersection of two circles. http://paulbourke.net/geometry/circlesphere
  3. Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci 340:250–261CrossRefGoogle Scholar
  4. Du X, Li S, Li G, Feng H, Chen S (2015) Stress wave tomography of wood internal defects using ellipse-based spatial interpolation and velocity compensation. BioResources 10(3):3948–3962CrossRefGoogle Scholar
  5. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874.  https://doi.org/10.1016/j.patrec.2005.10.010 CrossRefGoogle Scholar
  6. Feng H, Li G, Fu S, Wang X (2014) Tomographic image reconstruction using an interpolation method for tree decay detection. BioResources 9(2):3248–3263.  https://doi.org/10.15376/biores.9.2.3248-3263 CrossRefGoogle Scholar
  7. Gilbert EA, Smiley ET (2004) Picus sonic tomography for the quanification of decay in white oak (quercus alba) and hickory (carya spp.). J Arboric 30(5):277–281Google Scholar
  8. Gonçalves R, Reis MN, Ziller DP, Palma SS, A BM, (2017) Ultrasonic tomography in logs of urban trees with different levels and types of hollows. 20th international nondestructive testing and evaluation of wood symposium, Madison, Wisconsin, pp 172–178Google Scholar
  9. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall, Upper Saddle RiverGoogle Scholar
  10. Lin C, Kao Y, Lin T, Tsai M, Wang S, Lin L, Wang Y, Chan M (2008) Application of an ultrasonic tomographic technique for detecting defects in standing trees. Int Biodeter Biodegrad 62(4):434–441CrossRefGoogle Scholar
  11. Secco CB (2011) Detention of hollow in logs using methods of propagation of ultrasonic waves. Master’s Thesis, University of Campinas. http://repositorio.unicamp.br/jspui/handle/REPOSIP/256876
  12. Wedgwood F (1987) Data processing in ultrasonic ndt. Proc Ultrasonic Int 87:381–386CrossRefGoogle Scholar
  13. Yaitskova N, van de Kuilen JW (2014) Time-of-flight modeling of transversal ultrasonic scan of wood. J Acoust Soc Am 135(6):3409–3415CrossRefPubMedGoogle Scholar
  14. Zeng L, Lin J, Hua J, Shi W (2013) Interference resisting design for guided wave tomography. Smart Mater Struct 22(5):1–12.  https://doi.org/10.1088/0964-1726/22/5/055017 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jorge Renato Andrade Strobel
    • 1
  • Marco Antonio Garcia de Carvalho
    • 1
  • Raquel Gonçalves
    • 2
  • Cinthya Bertoldo Pedroso
    • 2
  • Mariana Nagle dos Reis
    • 2
  • Paulo S. Martins
    • 1
  1. 1.School of TechnologyUniversity of CampinasLimeiraBrazil
  2. 2.School of Agricultural EngineeringUniversity of CampinasCampinasBrazil

Personalised recommendations