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Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution

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Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11564))

Abstract

We study the pattern spectra in context of soil structure analysis. Good soil structure is vital for sustainable crop growth. Accurate and fast measuring methods can contribute greatly to soil management decisions. However, the current in-field approaches contain a degree of subjectivity, while obtaining quantifiable results through laboratory techniques typically involves sieving the soil which is labour- and time-intensive. We aim to replace this physical sieving process through image analysis, and investigate the effectiveness of pattern spectra to capture the size distribution of the soil aggregates. We calculate the pattern spectra from partitioning hierarchies in addition to the traditional max-tree. The study is posed as an image retrieval problem, and confirms the ability of pattern spectra and suitability of different partitioning trees to re-identify soil samples in different arrangements and scales.

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References

  1. Beare, M.H., Bruce, R.R.: A comparison of methods for measuring water-stable aggregates: implications for determining environmental effects on soil structure. Geoderma 56(1–4), 87–104 (1993)

    Article  Google Scholar 

  2. Bianconi, F., Di Maria, F., Micale, C., Fernández, A., Harvey, R.: Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Mach. Vis. Appl. 26(6), 775–789 (2015)

    Article  Google Scholar 

  3. Bosilj, P., Aptoula, E., Lefèvre, S., Kijak, E.: Retrieval of remote sensing images with pattern spectra descriptors. ISPRS Int. J. Geo-Inf. 5(12), 228 (2016)

    Article  Google Scholar 

  4. Bosilj, P., Damodaran, B.B., Aptoula, E., Mura, M.D., Lefèvre, S.: Attribute profiles from partitioning trees. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds.) ISMM 2017. LNCS, vol. 10225, pp. 381–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57240-6_31

    Chapter  Google Scholar 

  5. Breen, E.J., Jones, R.: Attribute openings, thinnings, and granulometries. Comput. Vis. Image Underst. 64(3), 377–389 (1996)

    Article  Google Scholar 

  6. Bronick, C.J., Lal, R.: Soil structure and management: a review. Geoderma 124(1–2), 3–22 (2005)

    Article  Google Scholar 

  7. Buscombe, D., Masselink, G.: Grain-size information from the statistical properties of digital images of sediment. Sedimentology 56(2), 421–438 (2009)

    Article  Google Scholar 

  8. Cavallaro, G., Falco, N., Dalla Mura, M., Benediktsson, J.: Automatic attribute profiles. IEEE Trans. Image Process. 26(4), 1859–1872 (2017)

    Article  MathSciNet  Google Scholar 

  9. Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps. J. Math. Imaging Vis. 60(4), 479–502 (2018)

    Article  MathSciNet  Google Scholar 

  10. Doulamis, A., Doulamis, N., Maragos, P.: Generalized multiscale connected operators with applications to granulometric image analysis. In: ICIP, vol. 3, pp. 684–687. IEEE (2001)

    Google Scholar 

  11. Frančišković-Bilinski, S., Bilinski, H., Vdović, N., Balagurunathan, Y., Dougherty, E.R.: Application of image-based granulometry to siliceous and calcareous estuarine and marine sediments. Estuar. Coast. Shelf Sci. 58(2), 227–239 (2003)

    Article  Google Scholar 

  12. Graham, D.J., Reid, I., Rice, S.P.: Automated sizing of coarse-grained sediments: image-processing procedures. Math. Geol. 37(1), 1–28 (2005)

    Article  Google Scholar 

  13. Guimarães, R.M.L., Ball, B.C., Tormena, C.A.: Improvements in the visual evaluation of soil structure. Soil Use Manag. 27(3), 395–403 (2011)

    Google Scholar 

  14. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  15. Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. 11(7), 701–716 (1989)

    Article  Google Scholar 

  16. Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)

    MATH  Google Scholar 

  17. Monasse, P., Guichard, F.: Scale-space from a level lines tree. J. Vis. Commun. Image Represent. 11(2), 224–236 (2000)

    Article  Google Scholar 

  18. Mora, C.F., Kwan, A.K.H., Chan, H.C.: Particle size distribution analysis of coarse aggregate using digital image processing. Cem. Concr. Res. 28(6), 921–932 (1998)

    Article  Google Scholar 

  19. Mueller, L., et al.: Visual assessment of soil structure: evaluation of methodologies on sites in canada, china and germany: Part I: comparing visual methods and linking them with soil physical data and grain yield of cereals. Soil Tillage Res. 103(1), 178–187 (2009)

    Article  Google Scholar 

  20. Pina, P., Lira, C., Lousada, M.: In-situ computation of granulometries of sedimentary grains-some preliminary results. J. Coast. Res. 64, 1727–1730 (2011)

    Google Scholar 

  21. Salehizadeh, M., Sadeghi, M.T.: Size distribution estimation of stone fragments via digital image processing. In: Bebis, G., et al. (eds.) ISVC 2010. LNCS, vol. 6455, pp. 329–338. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17277-9_34

    Chapter  Google Scholar 

  22. Salembier, P., Oliveras, A., Garrido, L.: Anti-extensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)

    Article  Google Scholar 

  23. Soille, P.: On genuine connectivity relations based on logical predicates. In: ICIAP, pp. 487–492. IEEE (2007)

    Google Scholar 

  24. Soille, P.: Constrained connectivity for hierarchical image partitioning and simplification. IEEE Trans. Pattern Anal. 30(7), 1132–1145 (2008)

    Article  Google Scholar 

  25. Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Trans. Pattern Anal. 29(2), 272–285 (2007)

    Article  Google Scholar 

  26. Urbach, E.R., Wilkinson, M.H.F.: Shape-only granulometries and grey-scale shape filters. In: ISMM, pp. 305–314 (2002)

    Google Scholar 

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Authors and Affiliations

  1. Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Brayford Campus, Lincoln, LN6 7TS, UK

    Petra Bosilj, Tom Duckett & Grzegorz Cielniak

  2. Lincoln Institute for Agri-food Technology, University of Lincoln, Riseholme Park, Lincoln, LN2 2LG, UK

    Petra Bosilj & Iain Gould

Authors
  1. Petra Bosilj
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  2. Iain Gould
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  3. Tom Duckett
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  4. Grzegorz Cielniak
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Corresponding author

Correspondence to Petra Bosilj .

Editor information

Editors and Affiliations

  1. Universität des Saarlandes, Saarbrücken, Germany

    Bernhard Burgeth

  2. Forschungszentrum Jülich GmbH, Jülich, Germany

    Andreas Kleefeld

  3. Université de Strasbourg, Illkirch Cedex, France

    Benoît Naegel

  4. Université de Reims Champagne-Ardenne, Reims Cedex 2, France

    Nicolas Passat

  5. Université Paris-Est, Noisy le Grand Cedex, France

    Benjamin Perret

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Bosilj, P., Gould, I., Duckett, T., Cielniak, G. (2019). Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-20867-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20866-0

  • Online ISBN: 978-3-030-20867-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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