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Grain-size assessment of fine and coarse aggregates through bipolar area morphology

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Abstract

This paper presents a new methodology for computing grey-scale granulometries and estimating the mean size of fine and coarse aggregates. The proposed approach employs area morphology and combines the information derived from both openings and closings to determine the size distribution. The method, which we refer to as bipolar area morphology (BAM), is general and can operate on particles of different size and shape. The effectiveness of the procedure was validated on a set of 13 classes of aggregates of size ranging from 0.125 to 16 mm and made a comparison with standard, fixed-shape granulometry. In the experiments our model consistently outperformed the standard approach and predicted the correct size class with overall accuracy over 92 %. Tests on three classes from real samples also confirmed the potential of the method for application in real scenarios.

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Notes

  1. To access the page: user \(=\) bipolar, psw \(=\) morphology.

Abbreviations

BAM:

Bipolar area morphology

PC:

Primary classes

RC:

Random classes

SC:

Secondary classes

\(\lambda \) :

Sieve scale (pixel)

\(\phi _\mathrm{m}\), \(\phi _\mathrm{M}\), \(\phi \) :

Minimum, maximum and expected true grain-size (mm)

\(\hat{\phi }\) :

Response of image-based granulometry (pixel)

\(\bar{\phi }\) :

Estimated mean grain size (mm)

\(\sigma \) :

Standard deviation

\(A_p\) :

Classification accuracy for the pth problem

\(\bar{A}\) :

Average classification accuracy

C :

Number of classes

\(\mathcal {C}\) :

Morphological closing

H :

Discrete cumulative size distribution

h :

Discrete pattern spectrum

\(\mathbf {I}\) :

Input image

\(\mathcal {O}\) :

Morphological opening

\(\mathcal {OC}\) :

Combined morphological opening and closing (bipolar morphology)

\(V(\mathbf {I})\) :

Volume of the input image (sum of pixels’ values)

P :

Number of subdivisions into train and test set (also referred to as number of problems or number of folds)

\(R^2\) :

Coefficient of determination

s :

Standard error

T :

Number of training samples

X :

Height of the input image

Y :

Width of the input image

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Authors and Affiliations

  1. Department of Engineering, Università degli Studi di Perugia, Via G. Duranti 93, 06125, Perugia, Italy

    Francesco Bianconi, Francesco Di Maria & Caterina Micale

  2. School of Industrial Engineering, Universidade de Vigo, Campus Universitario, 36310, Vigo, Spain

    Antonio Fernández

  3. School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK

    Richard W. Harvey

Authors
  1. Francesco Bianconi
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  2. Francesco Di Maria
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  3. Caterina Micale
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  4. Antonio Fernández
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  5. Richard W. Harvey
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Corresponding author

Correspondence to Francesco Bianconi.

Additional information

This work was supported by the European Commission under project LIFE12 ENV/IT/000411 ‘EMaRES—Enhanced Material Recovery and Environmental Sustainability for small scale waste management systems’.

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Bianconi, F., Di Maria, F., Micale, C. et al. Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Machine Vision and Applications 26, 775–789 (2015). https://doi.org/10.1007/s00138-015-0692-z

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